Search Results - Computation/Dataflow Optimization

MnnFast: A Fast and Scalable System Architecture for Memory-Augmented Neural Networks by Jang, Hanhwi, Kim, Joonsung, Jo, Jae-Eon, Lee, Jaewon, Kim, Jangwoo

“…Memory-augmented neural networks are getting more attention from many researchers as they can make an inference with the previous history stored in memory…”
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NLP-Fast: A Fast, Scalable, and Flexible System to Accelerate Large-Scale Heterogeneous NLP Models by Kim, Joonsung, Hur, Suyeon, Lee, Eunbok, Lee, Seungho, Kim, Jangwoo

“…: three end-to-end optimization techniques to accelerate…”
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Reconfigurable Dataflow Optimization for Spatiotemporal Spiking Neural Computation on Systolic Array Accelerators by Lee, Jeong-Jun, Li, Peng

“… Recognizing the need for efficient processing of complex spatiotemporal data while considering the all-or-none nature of spiking activities, we propose holistic reconfigurable dataflow optimization…”
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VQ-LLM: High-performance Code Generation for Vector Quantization Augmented LLM Inference by Liu, Zihan, Luo, Xinhao, Guo, Junxian, Ni, Wentao, Zhou, Yangjie, Guan, Yue, Guo, Cong, Cui, Weihao, Feng, Yu, Guo, Minyi, Zhu, Yuhao, Zhang, Minjia, Jin, Chen, Leng, Jingwen

“… and uncoordinated computation dataflow. Meanwhile, the diversity of VQ algorithms (e.g., different vector sizes and entry counts…”
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Techniques for Efficient Performance Analysis and Memory Optimization in Mapping Dataflow Models of Computation Onto Embedded Systems by Luna, Mauro Martín Letras

“…The power of modern multi-core and many-core platforms is an excellent fit for meeting the performance needs of embedded software applications. However, there…”
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A high-performance dataflow-centric optimization framework for deep learning inference on the edge by Zhang, Runhua, Jiang, Hongxu, Geng, Jinkun, Tian, Fangzheng, Ma, Yuhang, Wang, Haojie

“… Targeting the existing drawbacks of operator-centric frameworks, we design Xenos, which can automatically conduct dataflow-centric optimization of the computation graph and accelerate inference in two dimensions…”
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SWG: an architecture for sparse weight gradient computation by Wu, Weiwei, Tu, Fengbin, Li, Xiangyu, Wei, Shaojun, Yin, Shouyi

“… Nevertheless, exploiting the optimization opportunities would meet three underutilization problems, which are caused by (1…”
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AttentionLib: A Scalable Optimization Framework for Automated Attention Acceleration on FPGA by Liu, Zhenyu, Zhou, Xilang, Sun, Faxian, Chen, Jianli, Yu, Jun, Wang, Kun

“… AttentionLib automatically performs fusion dataflow optimization for attention computations and generates high-level synthesis code in compliance…”
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Efficient N:M Sparse DNN Training Using Algorithm, Architecture, and Dataflow Co-Design by Fang, Chao, Sun, Wei, Zhou, Aojun, Wang, Zhongfeng

“…Sparse training is one of the promising techniques to reduce the computational cost of DNNs while retaining high accuracy. In particular, N:M fine-grained…”
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Unleashing Network/Accelerator Co-Exploration Potential on FPGAs: A Deeper Joint Search by Lou, Wenqi, Gong, Lei, Wang, Chao, Qian, Jiaming, Wang, Xuan, Li, Changlong, Zhou, Xuehai

“…Recently, algorithm-hardware (HW) co-exploration for neural networks (NNs) has become the key to obtaining high-quality solutions. However, previous efforts…”
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Algorithm/Hardware Co-optimization for Sparsity-Aware SpMM Acceleration of GNNs by Gao, Yingxue, Gong, Lei, Wang, Chao, Wang, Teng, Li, Xi, Zhou, Xuehai

“… So in this paper, we demonstrate an algorithm/hardware co-optimization chance to enhance SpMM acceleration for GNNs…”
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SCnC: Efficient Unification of Streaming with Dynamic Task Parallelism by Sbîrlea, Dragoş, Shirako, Jun, Newton, Ryan, Sarkar, Vivek

“… This work shows that it is possible to exploit streaming as a safe and automatic optimization of a more general dataflow-based model…”
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DiMO-Sparse: Differentiable Modeling and Optimization of Sparse CNN Dataflow and Hardware Architecture by Song, Jianfeng, Liang, Rongjian, Gong, Yu, Yuan, Bo, Hu, Jiang

“… To the best of our knowledge, this paper presents the first systematic investigation of automatic dataflow and hardware optimization for sparse CNN computation…”
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Mechanisms Towards Energy-Efficient Dynamic Hardware Specialization by Ho, Chen-Han

“…In the past few decades, Von Neumann superscalar processors have been the prevalent approach for general purpose processing. Hardware specialization, as a…”
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An FPGA-based efficient accelerator for fault interaction of rupture dynamics by Yuan, Ming, Liu, Qiang, Gan, Lin

“…Efficiently predicting aftershocks based on rupture dynamics simulation is a crucial task in high-performance computing, traditionally dependent on…”
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SpikeFlow: A hardware–software co-designed systolic array for spiking neural networks by Wang, Jianan, Shi, Yang, Chen, Zhaoyun, Wen, Mei

“…Spiking neural networks (SNNs), often referred to as third-generation neural networks, offer substantial advantages in efficiency and power consumption, which…”
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CHAUS： Scalable VM-Based Channels for Unbounded Streaming by Zhang, Yu, Yu, Yu-Fen, Cao, Hui-Fang, Chen, Jian-Kang, Zhang, Qi-Liang

“…Stream processing is a special form of the dataflow execution model that offers extensive opportunities for optimization and automatic parallelism…”
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SPADE: Sparse Pillar-based 3D Object Detection Accelerator for Autonomous Driving by Lee, Minjae, Park, Seongmin, Kim, Hyungmin, Yoon, Minyong, Lee, Janghwan, Choi, Jun Won, Kim, Nam Sung, Kang, Mingu, Choi, Jungwook

“…3D object detection using point cloud (PC) data is essential for perception pipelines of autonomous driving, where efficient encoding is key to meeting…”
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Dataflow computing models, languages, and machines for intelligence computations by Herath, J., Yamaguchi, Y., Saito, N., Yuba, T.

“…The authors compare dataflow computing models, languages, and dataflow computing machines for numerical and nonnumerical computations. The…”
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PolyJuice: Detecting Mis-compilation Bugs in Tensor Compilers with Equality Saturation Based Rewriting by Zhou, Chijin, Qian, Bingzhou, Go, Gwihwan, Zhang, Quan, Li, Shanshan, Jiang, Yu

“… The main challenge is to construct equivalent graphs capable of efficiently exploring the diverse optimization logic during compilation…”
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