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2020 33nd International Conference on VLSI Design and 2020 19th International Conference on Embedded Systems (VLSID), 2020

The rapidly growing demands for powerful AI algorithms in many application domains have motivated massive investment in both high-quality deep neural network (DNN) models and high-efficiency implementations. In this position paper, we argue that a simultaneous DNN/implementation co-design methodology, named Neural Architecture and Implementation Search (NAIS), deserves more research attention to boost the development productivity and efficiency of both DNN models and implementation optimization. We propose a stylized design methodology that can drastically cut down the search cost while preserving the quality of the end solution. As an illustration, we discuss this DNN/implementation methodology in the context of both FPGAs and GPUs. We take autonomous driving as a key use case as it is one of the most demanding areas for high quality AI algorithms and accelerators. We discuss how such a co-design methodology can impact the autonomous driving industry significantly. We identify several research opportunities in this exciting domain.
International Conference on Computer-Aided Design, 2019

Multi-scale context module and single-stage encoder-decoder structure are commonly employed for semantic segmentation. The multi-scale context module refers to the operations to aggregate feature responses from a large spatial extent, while the single-stage encoder-decoder structure encodes the high-level semantic information in the encoder path and recovers the boundary information in the decoder path. In contrast, multi-stage encoder-decoder networks have been widely used in human pose estimation and show superior performance than their single-stage counterpart. However, few efforts have been attempted to bring this effective design to semantic segmentation. In this work, we propose a Semantic Prediction Guidance (SPG) module which learns to re-weight the local features through the guidance from pixel-wise semantic prediction. We find that by carefully re-weighting features across stages, a two-stage encoder-decoder network coupled with our proposed SPG module can significantly outperform its one-stage counterpart with similar parameters and computations. Finally, we report experimental results on the semantic segmentation benchmark Cityscapes, in which our SPGNet attains 81.1% on the test set using only ‘fine’ annotations.
International Conference on Computer Vision, 2019

Fine-grained action detection is an important task with numerous applications in robotics and human-computer interaction. Existing methods typically utilize a two-stage approach including extraction of local spatio-temporal features followed by temporal modeling to capture long-term dependencies. While most recent papers have focused on the latter (long-temporal modeling), here, we focus on producing features capable of modeling fine-grained motion more efficiently. We propose a novel locally-consistent deformable convolution, which utilizes the change in receptive fields and enforces a local coherency constraint to capture motion information effectively. Our model jointly learns spatio-temporal features (instead of using independent spatial and temporal streams). The temporal component is learned from the feature space instead of pixel space, e.g. optical flow. The produced features can be flexibly used in conjunction with other long-temporal modeling networks, e.g. ST-CNN, DilatedTCN, and ED-TCN. Overall, our proposed approach robustly outperforms the original long-temporal models on two fine-grained action datasets: 50 Salads and GTEA, achieving F1 scores of 80.22% and 75.39% respectively.
International Conference on Computer Vision, 2019

Recent advancements in deep learning techniques facilitate intelligentquery support in diverse applications, such as content-based image retrieval and audio texturing. Unlike conventional key-based queries, these intelligent queries lack efficient indexing and require complex compute operations for feature matching. To achieve highperformance intelligent querying against massive datasets, modern computing systems employ GPUs in-conjunction with solid-state drives (SSDs) for fast data access and parallel data processing. However, our characterization with various intelligent-query workloads developed with deep neural networks (DNNs), shows that the storage I/O bandwidth is still the major bottleneck that contributes 56%–90% of the query execution time. To this end, we present DeepStore, an in-storage accelerator architecture for intelligent queries. It consists of (1) energy-efficient in-storage accelerators designed specifically for supporting DNNbased intelligent queries, under the resource constraints in modern SSD controllers; (2) a similarity-based in-storage query cache to exploit the temporal locality of user queries for further performance improvement; and (3) a lightweight in-storage runtime system working as the query engine, which provides a simple software abstraction to support different types of intelligent queries. DeepStore exploits SSD parallelisms with design space exploration for achieving the maximal energy efficiency for in-storage accelerators. We validate DeepStore design with an SSD simulator, and evaluate it with a variety of vision, text, and audio based intelligent queries. Compared with the state-of-the-art GPU+SSD approach, DeepStore improves the query performance by up to 17.7×, and energy-efficiency by up to 78.6×.
Proceedings of the 52 Annual IEEE/ACM International Symposium on Microarchitecture (MICRO’19), 2019

Deep neural networks (DNNs) have been widely adopted in many domains, including computer vision, natural language processing, and medical care. Recent research reveals that sparsity in DNN parameters can be exploited to reduce inference computational complexity and improve network quality. However, sparsity also introduces irregularity and extra complexity in data processing, which make the accelerator design challenging. This work presents the design and implementation of a highly flexible sparse DNN inference accelerator on FPGA. Our proposed inference engine can be easily configured to be used in both mobile computing and high-performance computing scenarios. Evaluation shows our proposed inference engine effectively accelerates sparse DNNs and outperforms CPU solution by up to 4.7x in terms of energy efficiency.
IEEE High Performance Extreme Computing Conference, 2019

In automatic speech recognition (ASR), wideband (WB) and narrowband (NB) speech signals with different sampling rates typically use separate acoustic models. Therefore mixed-bandwidth (MB) acoustic modeling has important practical values for ASR system deployment. In this paper, we extensively investigate large-scale MB deep neural network acoustic modeling for ASR using 1,150 hours of WB data and 2,300 hours of NB data. We study various MB strategies including downsampling, upsampling and bandwidth extension for MB acoustic modeling and evaluate their performance on 8 diverse WB and NB test sets from various application domains. To deal with the large amounts of training data, distributed training is carried out on multiple GPUs using synchronous data parallelism.
International Speech Communication Association, 2019

Unlike traditional PCIe-based FPGA accelerators, heterogeneous SoC-FPGA devices provide tighter integrations between software running on CPUs and hardware accelerators. Modern heterogeneous SoC-FPGA platforms support multiple I/O cache coherence options between CPUs and FPGAs, but these options can have inadvertent effects on the achieved bandwidths depending on applications and data access patterns. To provide the most efficient communications between CPUs and accelerators, understanding the data transaction behaviors and selecting the right I/O cache coherence method is essential. In this paper, we use Xilinx Zynq UltraScale+ as the SoC platform to show how certain I/O cache coherence method can perform better or worse in different situations, ultimately affecting the overall accelerator performances as well. Based on our analysis, we further explore possible software and hardware modifications to improve the I/O performances with different I/O cache coherence options. With our proposed modifications, the overall performance of SoC design can be averagely improved by 20%.
International Conference on Field Programmable Logic and Applications 2019, 2019

In this paper, we present an update to our previous submission on k-truss decomposition from Graph Challenge 2018. For single GPU k-truss implementation, we propose multiple algorithmic optimizations that significantly improve performance by up to 35.2x (6.9x on average) compared to our previous GPU implementation. In addition, we present a scalable multi-GPU implementation in which each GPU handles a different ‘k’ value. Compared to our prior multi-GPU implementation,the proposed approach is faster by up to 151.3x (78.8x on average). In case when the edges with only maximal k-truss are sought, incrementing the `k’ value in each iteration is inefficient particularly for graphs with large maximum k-truss. Thus, we propose binary search for the ‘k’ value to find the maximal k-truss. The binary search approach on a single GPU is up to 101.5 (24.3x on average) faster than our 2018 $k$-truss submission. Lastly, we show that the proposed binary search finds the maximum k-truss for “Twitter” graph dataset having 2.8 billion bidirectional edges in just 16 minutes on a single V100 GPU.
2019 IEEE High Performance Extreme Computing Conference, 2019

This work presents an update to the triangle-counting portion of the subgraph isomorphism static graph challenge. This work is motivated by a desire to understand the impact of CUDA unified memory on the triangle-counting problem. First, CUDA unified memory is used to overlap reading large graph data from disk with graph data structures in GPU memory. Second, we use CUDA unified memory hintsto solve multi-GPU performance scaling challenges present in our last submission. Finally, we improve the single-GPU kernel performance from our past submission by introducing a work-stealing dynamic algorithm GPU kernel with persistent threads, which makes performance adaptive for large graphs withoutrequiring a graph analysis phase.
2019 IEEE High Performance Extreme Computing Conference, 2019

Deep Neural Networks (DNNs) have become promising solutions for data analysis especially for raw data processing from sensors. However, using DNN-based approaches can easily introduce huge demands of computation and memory consumption, which may not be feasible for direct deployment onto the Internet of Thing (IoT) devices, since they have strict constraints on hardware resources, power budgets, response latency, and manufacturing cost. To bring DNNs into IoT devices, embedded FPGA can be one of the most suitable candidates by providing better energy efficiency than GPU and CPU based solutions, and higher flexibility than ASICs. In this paper, we propose a systematic solution to deploy DNNs on embedded FPGAs, which includes a ternarized hardware Deep Learning Accelerator (T-DLA), and a framework for ternary neural network (TNN) training. T-DLA is a highly optimized hardware unit in FPGA specializing in acelerating the TNNs, while the proposed framework can significantly compress the DNN parameters down to two bits with little accuracy drop. Results show that our training framework can compress the DNN up to 14.14X while maintaining nearly the same accuracy compared to the floating point version. By illustrating our proposed design techniques, the T-DLA can deliver up to 0.4TOPS with 2.576W power consumption, showing 873.6X and 5.1X higher energy efficiency (fps/W) on ImageNet with Resnet-18 model comparing to Xeon E5-2630 CPU and Nvidia 1080 Ti GPU. To the best of our knowledge, this is the first instruction-based highly efficient ternary DLA design reported from the literature.
IEEE Computer Society Annual Symposium on VLSI, 2019

The slow down in Moore’s Law has resulted in poor scaling of performance and energy. This slow down in scaling has been accompanied by the explosive growth of cognitive computing applications, creating a demand for high performance and energy efficient solutions. Amidst this climate, FPGA-based accelerators are emerging as a potential platform for deploying accelerators for cognitive computing workloads. However, the slow-down in scaling also limits the scaling of memory and I/O bandwidths. Additionally, a growing fraction of energy is spent on data transfer between off-chip memory and the compute units. Thus, now more than ever, there is a need to leverage near-memory and in-storage computing to maximize the bandwidth available to accelerators, and further improve energy efficiency. In this paper, we make the case for leveraging FPGAs in near-memory and in-storage settings, and present opportunities and challenges in such scenarios. We introduce a conceptual FPGA-based near-data processing architecture, and discuss innovations in architecture, systems, and compilers for accelerating cognitive computing workloads..
IEEE Computer Society Annual Symposium on VLSI, 2019

Text style transfer rephrases a text from a source style (eg, informal) to a target style (eg, formal) while keeping its original meaning. Despite the success existing works have achieved using a parallel corpus for the two styles, transferring text style has proven significantly more challenging when there is no parallel training corpus. In this paper, we address this challenge by using a reinforcement-learning-based generator-evaluator architecture. Our generator employs an attention-based encoder-decoder to transfer a sentence from the source style to the target style. Our evaluator is an adversarially trained style discriminator with semantic and syntactic constraints that score the generated sentence for style, meaning preservation, and fluency. Experimental results on two different style transfer tasks (sentiment transfer and formality transfer) show that our model outperforms state-of-the-art approaches. Furthermore, we perform a manual evaluation that demonstrates the effectiveness of the proposed method using subjective metrics of generated text quality.
Proceedings of the 2019 Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies, 2019

While embedded FPGAs are attractive platforms for DNN acceleration on edge-devices due to their low latency and high energy efficiency, the scarcity of resources of edge-scale FPGA devices also makes it challenging for DNN deployment. In this paper, we propose a simultaneous FPGA/DNN co-design methodology with both bottom-up and top-down approaches: a bottom-up hardware-oriented DNN model search for high accuracy, and a top-down FPGA accelerator design considering DNN-specific characteristics. We also build an automatic co-design flow, including an Auto-DNN engine to perform hardware-oriented DNN model search, as well as an Auto-HLS engine to generate synthesizable C code of the FPGA accelerator for explored DNNs. We demonstrate our co-design approach on an object detection task using PYNQ-Z1 FPGA. Results show that our proposed DNN model and accelerator outperform the state-of-the-art FPGA designs in all aspects including Intersection-over-Union (IoU) (6.2% higher), frames per second (FPS) (2.48X higher), power consumption (40% lower), and energy efficiency (2.5X higher). Compared to GPU-based solutions, our designs deliver similar accuracy but consume far less energy.
Design Automation Conference, 2019

Using flash-based solid state drives (SSDs) as main memory has been proposed as a practical solution towards scaling memory capacity for data-intensive applications. However, almost all existing approaches rely on the paging mechanism to move data between SSDs and host DRAM. This inevitably incurs significant performance overhead and extra I/O traffic. Thanks to the byte-addressability supported by the PCIe interconnect and the internal memory in SSD controllers, it is feasible to access SSDs in both byte and block granularity today. Exploiting the benefits of SSD’s byte-accessibility in today’s memory-storage hierarchy is, however, challenging as it lacks systems support and abstractions for programs. In this paper, we present FlatFlash, an optimized unified memory-storage hierarchy, to efficiently use byte-addressable SSD as part of the main memory. We extend the virtual memory management to provide a unified memory interface so that programs can access data across SSD and DRAM in byte granularity seamlessly. We propose a lightweight, adaptive page promotion mechanism between SSD and DRAM to gain benefits from both the byte-addressable large SSD and fast DRAM concurrently and transparently, while avoiding unnecessary page movements. Furthermore, we propose an abstraction of byte-granular data persistence to exploit the persistence nature of SSDs, upon which we rethink the design primitives of crash consistency of several representative software systems that require data persistence, such as file systems and databases. Our evaluation with a variety of applications demonstrates that, compared to the current unified memory-storage systems, FlatFlash improves the performance for memory-intensive applications by up to 2.3x, reduces the tail latency for latency-critical applications by up to 2.8x, scales the throughput for transactional database by up to 3.0x, and decreases the meta-data persistence overhead for file systems by up to 18.9x. FlatFlash also improves the cost-effectiveness by up to 3.8x compared to DRAM-only systems, while enhancing the SSD lifetime significantly.
Proceedings of the Twenty-Fourth International Conference on Architectural Support for Programming Languages and Operating Systems (ASPLOS’19), 2019

2019 ACM/SPEC International Conference on Performance Engineering, 2019

Misspelled words of the malicious kind work by changing specific keywords and are intended to thwart existing automated applications for cyber-environment control such as harassing content detection on the Internet and email spam detection. In this paper, we focus on malicious spelling correction, which requires an approach that relies on the context and the surface forms of targeted keywords. In the context of two applications-profanity detection and email spam detection-we show that malicious misspellings seriously degrade their performance. We then propose a context-sensitive approach for malicious spelling correction using word embeddings and demonstrate its superior performance compared to state-of-the-art spell checkers.
The World Wide Web Conference, 2019

The efficacy and effectiveness of Convolutional Neural Networks (CNNs) have been proven in a wide range of machine learning applications. However, the high computational complexity of CNNs presents a critical challenge towards their broader adoption in real-time and power-efficient scenarios. FPGAs are poised to take a significant role for high-performance and energy-efficient computation of CNNs for both mobile (e.g., UAVs, self-driving cars, and IoT devices) and cloud computing domains. However, implementing an effective CNN system onto FPGAs efficiently remains problematic. The current cloud-based FPGAs with unique design constraints and architectural characteristics further increase the challenges. To address these challenges, we propose a novel open-source automated tool chain called Cloud-DNN. Our tool chain takes trained CNN models specified in Caffe as input, performs a set of transformations, and maps the model to a cloud-based FPGA. Cloud-DNN can significantly improve the overall design productivity of CNNs on FPGAs while satisfying the emergent computational requirements. Our design provides an alternative solution compared to other cloud-based options (e.g., GPUs or TPUs) while offering flexible, and high performance DNN inferences. The unique features of Cloud-DNN include the optimizations with cloud-platform characteristics and the support of easier and streamlined implementation. Experimental results demonstrate up to 104.55x performance improvement when compared to CPU implementation and comparable usability, flexibility, and strong quality compared to other state-of-the-art DNN inference implementations on standalone FPGAs.
Proceedings of the 2019 ACM/SIGDA International Symposium on Field-Programmable Gate Arrays, 2019

Neural machine translation (NMT) is a popular topic in Natural Language Processing which uses deep neural networks (DNNs) for translation from source to targeted languages. With the emerging technologies, such as bidirectional Gated Recurrent Units (GRU), attention mechanisms, and beam-search algorithms, NMT can deliver improved translation quality compared to the conventional statistics-based methods, especially for translating long sentences. However, higher translation quality means more complicated models, higher computation/memory demands, and longer translation time, which causes difficulties for practical use. In this paper, we propose a design methodology for implementing the inference of a real-life NMT (with the problem size = 172 GFLOP) on FPGA for improved run time latency and energy efficiency. We use HighLevel Synthesis (HLS) to build high-performance parameterized IPs for handling the most basic operations (multiply-accumulations) and construct these IPs to accelerate the matrix-vector multiplication (MVM) kernels, which are frequently used in NMT. Also, we perform a design space exploration by considering both computation resources and memory access bandwidth when utilizing the hardware parallelism in the model and generate the best parameter configurations of the proposed IPs. Accordingly, we propose a novel hybrid parallel structure for accelerating the NMT with affordable resource overhead for the targeted FPGA. Our design is demonstrated on a Xilinx VCU118 with overall performance at 7.16 GFLOPS.
Asia and South Pacific Design Automation Conference, 2019

ArXiv 2018, 2018

Building a high-performance EPGA accelerator for Deep Neural Networks (DNNs) often requires RTL programming, hardware verification, and precise resource allocation, all of which can be time-consuming and challenging to perform even for seasoned FPGA developers. To bridge the gap between fast DNN construction in software (e.g., Caffe, TensorFlow) and slow hardware implementation, we propose DNNBuilder for building high-performance DNN hardware accelerators on FPGAs automatically. Novel techniques are developed to meet the throughput and latency requirements for both cloud- and edge-devices. A number of novel techniques including high-quality RTL neural network components, a fine-grained layer-based pipeline architecture, and a column-based cache scheme are developed to boost throughput, reduce latency, and save FPGA on-chip memory. To address the limited resource challenge, we design an automatic design space exploration tool to generate optimized parallelism guidelines by considering external memory access bandwidth, data reuse behaviors, FPGA resource availability, and DNN complexity. DNNBuilder is demonstrated on four DNNs (Alexnet, ZF, VGG16, and YOLO) on two FPGAs (XC7Z045 and KU115) corresponding to the edge- and cloud-computing, respectively. The fine-grained layer-based pipeline architecture and the column-based cache scheme contribute to 7.7x and 43x reduction of the latency and BRAM utilization compared to conventional designs. We achieve the best performance (up to 5.15x faster) and efficiency (up to 5.88x more efficient) compared to published FPGA-based classification-oriented DNN accelerators for both edge and cloud computing cases. We reach 4218 GOPS for running object detection DNN which is the highest throughput reported to the best of our knowledge. DNNBuilder can provide millisecond-scale real-time performance for processing HD video input and deliver higher efficiency (up to 4.35x) than the GPU-based solutions.
2018 International Conference On Computer Aided Design, 2018

FPGAs have been rapidly adopted for acceleration of Deep Neural Networks (DNNs) with improved latency and energy efficiency compared to CPU and GPU-based implementations. High-level synthesis (HLS) is an effective design flow for DNNs due to improved productivity, debugging, and design space exploration ability. However, optimizing large neural networks under resource constraints for FPGAs is still a key challenge. In this paper, we present a series of effective design techniques for implementing DNNs on FPGAs with high performance and energy efficiency. These include the use of configurable DNN IPs, performance and resource modeling, resource allocation across DNN layers, and DNN reduction and re-training. We showcase several design solutions including Long-term Recurrent Convolution Network (LRCN) for video captioning, Inception module for FaceNet face recognition, as well as Long Short-Term Memory (LSTM) for sound recognition. These and other similar DNN solutions are ideal implementations to be deployed in vision or sound based IoT applications.
International Conference on Computer-Aided Design, 2018

The physical memory capacity of servers is expected to increase drastically with deployment of the forthcoming non-volatile memory technologies. This is a welcomed improvement for emerging data-intensive applications. For such servers to be cost-effective, nonetheless, we must cost-effectively increase compute throughput and memory bandwidth commensurate with the increase in memory capacity without compromising application readiness. Tackling this challenge, we present Memory Channel Network (MCN) architecture in this paper. Specifically, first, we propose an MCN DIMM, an extension of a buffered DIMM where a small but capable processor called MCN processor is integrated with a buffer device on the DIMM for near-memory processing. Second, we implement device drivers to give the host and MCN processors in a server an illusion that they are independent heterogeneous nodes connected through an Ethernet link. These allow the host and MCN processors in a server to run a given data-intensive application together based on popular distributed computing frameworks such as MPI and Spark without any change in the host processor hardware and its application software, while offering the benefits of high-bandwidth and low-latency communications between the host and the MCN processors over memory channels. As such, MCN can serve as an application-transparent framework which can seamlessly unify near-memory processing within a server and distributed computing across such servers for data-intensive applications. Our simulation running the full software stack shows that a server with 8 MCN DIMMs offers 4.56X higher throughput and consume 47.5% less energy than a cluster with 9 conventional nodes connected through Ethernet links, as it facilitates up to 8.17X higher aggregate DRAM bandwidth utilization. Lastly, we demonstrate the feasibility of MCN with an IBM POWER8 system and an experimental buffered DIMM.
The 51st Annual IEEE/ACM International Symposium on Microarchitecture, 2018

The production of sports highlight packages summarizing a game’s most exciting moments is an essential task for broadcast media. Yet, it requires labor-intensive video editing. We propose a novel approach for auto-curating sports highlights, and demonstrate it to create a first of a kind, real-world system for the editorial aid of golf and tennis highlight reels. Our method fuses information from the players’ reactions (action recognition such as high-fives and fist pumps), players’ expressions (aggressive, tense, smiling, and neutral), spectators (crowd cheering), commentator (tone of the voice and word analysis), and game analytics to determine the most interesting moments of a game. We accurately identify the start and end frames of key shot highlights with additional metadata, such as the player’s name and the whole number, or analysts input allowing personalized content summarization and retrieval. In addition, we introduce new techniques for learning our classifiers with reduced manual training data annotation by exploiting the correlation of different modalities. Our work has been demonstrated at a major golf tournament (2017 Masters) and two major international tennis tournaments (2017 Wimbledon and U.S. Open), successfully extracting highlights through the course of the sporting events. For the 2017 Masters, 54% of the clips selected by our system overlapped with the official highlights reels. Furthermore, user studies showed that 90% of the non-overlapping ones were of the same quality of the official clips for the 2017 Masters, while the automatic selection of clips for highlights of 2017 Wimbledon and 2017 US Open agreed with human preferences 80% and 84.2% of the time, respectively.
IEEE Transactions on Multimedia, 2018

Prepositions are highly polysemous, and their variegated senses encode significant seman- tic information. In this paper we match each preposition’s left- and right context, and their interplay to the geometry of the word vec- tors to the left and right of the preposition. Extracting these features from a large corpus and using them with machine learning models makes for an efficient preposition sense dis- ambiguation (PSD) algorithm, which is com- parable to and better than state-of-the-art on two benchmark datasets. Our reliance on no linguistic tool allows us to scale the PSD al- gorithm to a large corpus and learn sense- specific preposition representations. The cru- cial abstraction of preposition senses as word representations permits their use in downstream applications–phrasal verb paraphrasing and preposition selection–with new state-of- the-art results.
Proceedings of the 2018 Conference on Empirical Methods in Natural Language Processing, 2018

In this paper, we present an update to our previous submission from Graph Challenge 2017. This work describes and evaluates new software algorithm optimizations undertaken for our 2018 year submission on Collaborative CPU+GPU Algorithms for Triangle Counting and Truss Decomposition. First, we describe four major optimizations for the triangle counting which improved performance by up to 117x over our prior submission. Additionally, we show that our triangle-counting algorithm is on average 151.7x faster than NVIDIA’s NVGraph library (max 476x) for SNAP datasets. Second, we propose a novel parallel k-truss decomposition algorithm that is time-efficient and is up to 13.9x faster than our previous submission. Third, we evaluate the effect of generational hardware improvements between the IBM “Minsky” (POWER8, P100, NVLink 1.0) and “Newell” (POWER9, V100, NVLink 2.0) platforms. Lastly, the software optimizations presented in this work and the hardware improvements in the Newell platform enable analytics and discovery on large graphs with millions of nodes and billions of edges in less than a minute. In sum, the new algorithmic implementations are significantly faster and can handle much larger “big” graphs.
2018 IEEE High Performance extreme Computing Conference (HPEC), 2018

This work provides a simple approach to discover tight object bounding boxes with only image-level supervision, called Tight box mining with Surrounding Segmentation Context (TS2C). We observe that object candidates mined through current multiple instance learning methods are usually trapped to discriminative object parts, rather than the entire object. TS2C leverages surrounding segmentation context derived from weakly-supervised segmentation to suppress such low-quality distracting candidates and boost the high-quality ones. Specifically, TS2C is developed based on two key properties of desirable bounding boxes: 1) high purity, meaning most pixels in the box are with high object response, and 2) high completeness, meaning the box covers high object response pixels comprehensively. With such novel and computable criteria, more tight candidates can be discovered for learning a better object detector. With TS2C, we obtain 48.0% and 44.4% mAP scores on VOC 2007 and 2012 benchmarks, which are the new state-of-the-arts.
European Conference on Computer Vision, 2018

European Conference on Computer Vision, 2018

Recent region-based object detectors are usually built with separate classification and localization branches on top of shared feature extraction networks. In this paper, we analyze failure cases of state-of-the-art detectors and observe that most hard false positives result from classification instead of localization. We conjecture that: (1) Shared feature representation is not optimal due to the mismatched goals of feature learning for classification and localization; (2) multi-task learning helps, yet optimization of the multi-task loss may result in sub-optimal for individual tasks; (3) large receptive field for different scales leads to redundant context information for small objects.We demonstrate the potential of detector classification power by a simple, effective, and widely-applicable Decoupled Classification Refinement (DCR) network. DCR samples hard false positives from the base classifier in Faster RCNN and trains a RCNN-styled strong classifier. Experiments show new state-of-the-art results on PASCAL VOC and COCO without any bells and whistles.
European Conference on Computer Vision, 2018

ArXiv 2018, 2018

Neural network accelerators with low latency and low energy consumption are desirable for edge computing. To create such accelerators, we propose a design flow for accelerating the extremely low bit-width neural network (ELB-NN) in embedded FPGAs with hybrid quantization schemes. This flow covers both network training and FPGA-based network deployment, which facilitates the design space exploration and simplifies the tradeoff between network accuracy and computation efficiency. Using this flow helps hardware designers to deliver a network accelerator in edge devices under strict resource and power constraints. We present the proposed flow by supporting hybrid ELB settings within a neural network. Results show that our design can deliver very high performance peaking at 10.3 TOPS and classify up to 325.3 image/s/watt while running large-scale neural networks for less than 5W using embedded FPGA. To the best of our knowledge, it is the most energy efficient solution in comparison to GPU or other FPGA implementations reported so far in the literature.
28th International Conference on Field-Programmable Logic and Applications, 2018

Many modern embedded CPUs adopt Non-Coherent DMA (NC-DMA) over Coherent DMA (C-DMA) because of simplicity. An NC-DMA design, however, requires a CPU device driver to explicitly invalidate or flush a wide range of cache space. When an I/O DMA device writes data to a main memory region, the CPU needs to invalidate the cache space corresponding to the same memory region twice: (1) to prevent dirty cache lines from overwriting the DMA data and (2) to remove any cache lines prefetched before the DMA is done. In this work, we first show that such explicit invalidations consume 31 percent of CPU cycles, limiting the data transfer throughput of a high-speed network interface card (NIC) when receiving network packets. Second, we propose a Semi-Coherent DMA (SC-DMA) architecture for improving the efficiency of NC-DMA with a slight modification to the hardware. Specifically, our SC-DMA records the DMA region and prohibits any data that is prefetched from the region from entering the cache, reducing nearly 50 percent of the unnecessary invalidations. Lastly, we identify several software optimizations that can substantially reduce excessive cache invalidations prevalent in NIC drivers. Our evaluation with NVIDIA Jetson TX2 shows that our proposed SC-DMA design with the NIC driver optimizations can improve the NIC data transfer throughput by up to 53.3 percent.
IEEE Computer Architecture Letters, 2018

Frontiers in ICT, 2018

The 56th Annual Meeting of the Association for Computational Linguistics, 2018

Measuring similarity between texts is an important task for several applications. Available approaches to measure document similarity are inadequate for document pairs that have non-comparable lengths, such as a long document and its summary. This is because of the lexical, contextual and the abstraction gaps between a long document of rich details and its concise summary of abstract information. In this paper, we present a document matching approach to bridge this gap, by comparing the texts in a common space of hidden topics. We evaluate the matching algorithm on two matching tasks and find that it consistently and widely outperforms strong baselines. We also highlight the benefits of incorporating domain knowledge to text matching.
The 56th Annual Meeting of the Association for Computational Linguistics, 2018

The 56th Annual Meeting of the Association for Computational Linguistics, 2018

International Workshop on OpenPOWER for HPC (IWOPH’18) at the 2018 ISC High Performance Conference, 2018

the Ninth International Conference on Computational Creativity, 2018

the Ninth International Conference on Computational Creativity, 2018

The 55th Design Automation Conference (DAC) Late Breaking Results, 2018

The production of sports highlight packages summarizing a game’s most exciting moments is an essential task for broadcast media. Yet, it requires labor-intensive video editing. We propose a novel approach for auto-curating sports highlights, and demonstrate it to create a first of a kind, real-world system for the editorial aid of golf and tennis highlight reels. Our method fuses information from the players’ reactions (action recognition such as high-fives and fist pumps), players’ expressions (aggressive, tense, smiling and neutral), spectators (crowd cheering), commentator (tone of the voice and word analysis) and game analytics to determine the most interesting moments of a game.
The 30th IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR) workshop on Sight and Sound, 2018

The 30th IEEE/CVF Conference on Computer Vision and Pattern Recognition, 2018

The 30th IEEE/CVF Conference on Computer Vision and Pattern Recognition, 2018

The 30th IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR) workshop on Nvidia City Challenge, 2018

We propose a network for Congested Scene Recognition called CSRNet to provide a data-driven and deep learning method that can understand highly congested scenes and perform accurate count estimation as well as present high-quality density maps. The proposed CSRNet is composed of two major components: a convolutional neural network (CNN) as the front-end for 2D feature extraction and a dilated CNN for the back-end, which uses dilated kernels to deliver larger reception fields and to replace pooling operations. CSRNet is an easy-trained model because of its pure convolutional structure. We demonstrate CSRNet on four datasets (ShanghaiTech dataset, the UCF_CC_50 dataset, the WorldEXPO’10 dataset, and the UCSD dataset) and we deliver the state-of-the-art performance. In the ShanghaiTech Part_B dataset, CSRNet achieves 47.3% lower Mean Absolute Error (MAE) than the previous state-of-the-art method. We extend the targeted applications for counting other objects, such as the vehicle in TRANCOS dataset. Results show that CSRNet significantly improves the output quality with 15.4% lower MAE than the previous state-of-the-art approach.
The 30th IEEE/CVF Conference on Computer Vision and Pattern Recognition, 2018

The 30th IEEE/CVF Conference on Computer Vision and Pattern Recognition, 2018

The Seventh Joint Conference on Lexical and Computational Semantics, 2018

The 16th Annual Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies, 2018

Prepositions are among the most frequent words in English and play complex roles in the syntax and semantics of sentences. Not surprisingly, they pose well-known difficulties in automatic processing of sentences (prepositional attachment ambiguities and idiosyncratic uses in phrases). Existing methods on preposition representation treat prepositions no different from content words (eg, word2vec and GloVe). In addition, recent studies aiming at solving prepositional attachment and preposition selection problems depend heavily on external linguistic resources and use dataset-specific word representations. In this paper we use word-triple counts (one of the triples being a preposition) to capture a preposition’s interaction with its attachment and complement. We then derive preposition embeddings via tensor decomposition on a large unlabeled corpus. We reveal a new geometry involving Hadamard products and empirically demonstrate its utility in paraphrasing phrasal verbs. Furthermore, our preposition embeddings are used as simple features in two challenging downstream tasks: preposition selection and prepositional attachment disambiguation. We achieve results comparable to or better than the state-of-the-art on multiple standardized datasets.
The 16th Annual Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies, 2018

Deep Convolutional Neural Networks have become a Swiss knife in solving critical artificial intelligence tasks. However, deploying deep CNN models for latency-critical tasks remains to be challenging because of the complex nature of CNNs. Recently, FPGA has become a favorable device to accelerate deep CNNs thanks to its high parallel processing capability and energy efficiency. In this work, we explore different fast convolution algorithms including Winograd and Fast Fourier Transform (FFT), and find an optimal strategy to apply them together on different types of convolutions. We also propose an optimization scheme to exploit parallelism on novel CNN architectures such as Inception modules in GoogLeNet. We implement a configurable IP-based face recognition acceleration system based on FaceNet using High-Level Synthesis. Our implementation on a Xilinx Ultrascale device achieves 3.75x latency speedup compared to a high-end NVIDIA GPU and surpasses previous FPGA results significantly.
The 28th edition of the ACM Great Lakes Symposium on VLSI, 2018

The 26th IEEE International Symposium on Field-Programmable Custom Computing Machines, 2018

2018 IEEE International Conference on Acoustics, 2018

52th Annual Conference on Information Sciences and Systems, 2018

OpenPOWER US Submit, 2018

2018 Information Theory and Applications Workshop, 2018

In the Thirty-first Annual Conference on Neural Information Processing Systems., 2017

the Thirty-first Annual Conference on Neural Information Processing Systems, 2017

IEEE Information Theory Society Newsletter, 2017

IEEE international conference on Rebooting Computing, 2017

IEEE High Performance Extreme Computing Conference, 2017

In The 2017 Conference on Empirical Methods on Natural Language Processing., 2017

Conference on Empirical Methods in Natural Language Processing, 2017

FPGA is a promising candidate for the acceleration of Deep Neural Networks (DNN) with improved latency and energy consumption compared to CPU and GPU-based implementations. DNNs use sequences of layers of regular computation that are well suited for HLS-based design for FPGA. However, optimizing large neural networks under resource constraints is still a key challenge. HLS must manage on-chip computation, buffering resources, and off-chip memory accesses to minimize the total latency. In this paper, we present a design framework for DNNs that uses highly configurable IPs for neural network layers together with a new design space exploration engine for Resource Allocation Management (REALM). We also carry out efficient memory subsystem design and fixed-point weight re-training to further improve our FPGA solution. We demonstrate our design framework on the Long-term Recurrent Convolution Network for video inputs. Our implementation on a Xilinx VC709 board achieves 3.1X speedup compared to an NVIDIA K80 and 4.75X speedup compared to an Intel Xeon with 17.5X lower energy per image.
the 27th International Conference on Field-Programmable Logic and Applications, 2017

KDD Workshop on Data-Driven Discovery, 2017

SIGKDD, 2017

SIGKDD, 2017

the Third IEEE Smart World Congress, 2017

the Conference on Natural Language Learning, 2017

ACL, 2017

CVPR 2017, 2017

IEEE Micro Magazine, 2017

the 10th International Conference on Educational Data Mining, 2017

International Symposium on Information Theory, 2017

presented at NetSci: International School and Conference on Network Science, 2017

Poster for the 8th International Conference on Computational Creativity, 2017

arXiv, 2017

GPUs are capable of running a variety of applications, however their generic parallel-architecture can lead to inefficient use of resources and reduced power efficiency, due to algorithmic or architectural constraints. In this work, taking inspiration from CGRAs (coarse-grained reconfigurable architectures), we demonstrate resource sharing and re-distribution as a solution that can be leveraged by reconfiguring the GPU on a kernel-by-kernel basis. We explore four different schemes that trade the number of active SMs (streaming multiprocessor) for increased occupancy and local memory resources per SM and demonstrate improved power and energy with limited impact to performance. Our most aggressive scheme, BigSM, is capable of saving energy by up to 54%, and 26% on an average.
IEEE International Symposium on Field-Programmable Custom Computing Machines, 2017

in Proceedings of the 5th International Conference on Learning Representations, 2017

in Proceedings of the 2017 Information Theory and its Applications Workshop, 2017

This paper proposes a simple test for compositionality (i.e., literal usage) of a word or phrase in a context-specific way. The test is computationally simple, relying on no external resources and only uses a set of trained word vectors. Experiments show that the proposed method is competitive with state of the art and displays high accuracy in context-specific compositionality detection of a variety of natural language phenomena (idiomaticity, sarcasm, metaphor) for different datasets in multiple languages. The key insight is to connect compositionality to a curious geometric property of word embeddings, which is of independent interest.
Proceedings of the 31st AAAI Conference, 2017

INFORMS Annual Meeting, 2016