Proceedings ArticleDOI
SCNN: An Accelerator for Compressed-sparse Convolutional Neural Networks
Angshuman Parashar,Minsoo Rhu,Anurag Mukkara,Antonio Puglielli,Rangharajan Venkatesan,Brucek Khailany,Joel Emer,Stephen W. Keckler,William J. Dally +8 more
- Vol. 45, Iss: 2, pp 27-40
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TLDR
The Sparse CNN (SCNN) accelerator as discussed by the authors employs a dataflow that enables maintaining the sparse weights and activations in a compressed encoding, which eliminates unnecessary data transfers and reduces storage requirements.Abstract:
Convolutional Neural Networks (CNNs) have emerged as a fundamental technology for machine learning. High performance and extreme energy efficiency are critical for deployments of CNNs, especially in mobile platforms such as autonomous vehicles, cameras, and electronic personal assistants. This paper introduces the Sparse CNN (SCNN) accelerator architecture, which improves performance and energy efficiency by exploiting the zero-valued weights that stem from network pruning during training and zero-valued activations that arise from the common ReLU operator. Specifically, SCNN employs a novel dataflow that enables maintaining the sparse weights and activations in a compressed encoding, which eliminates unnecessary data transfers and reduces storage requirements. Furthermore, the SCNN dataflow facilitates efficient delivery of those weights and activations to a multiplier array, where they are extensively reused; product accumulation is performed in a novel accumulator array. On contemporary neural networks, SCNN can improve both performance and energy by a factor of 2.7x and 2.3x, respectively, over a comparably provisioned dense CNN accelerator.read more
Citations
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Journal ArticleDOI
BitXpro: Regularity-Aware Hardware Runtime Pruning for Deep Neural Networks
TL;DR: BitXpro as mentioned in this paper targets the bit-level sparsity and the sparsity irregularity in the parameters and pinpoints and prunes the useless bits on-the-fly in the proposed BitXpro accelerator.
Proceedings ArticleDOI
Session 13 overview: Machine learning and signal processing: Digital architectures and systems subcommittee
TL;DR: To further support increased requirements for multiuser connectivity and sparse data,Multiuser MIMO and compressive reconstruction are also required.
Journal ArticleDOI
Multiobjective End-to-End Design Space Exploration of Parameterized DNN Accelerators
TL;DR: In this article , an end-to-end Pareto optimization of DNN accelerators is presented, whose goal is to determine the accelerator's architecture and the mapping for each layer that optimizes end to end and in a multiobjective fashion a set of conflicting design criteria.
Posted Content
GPTPU: Accelerating Applications using Edge Tensor Processing Units
Kuan-Chieh Hsu,Hung-Wei Tseng +1 more
TL;DR: GPTPU as mentioned in this paper is an open-source, open-architecture framework that allows the developer and research communities to discover opportunities that NN accelerators enable for applications by leveraging the underlying edge TPUs to perform tensor-algorithm-based compute kernels.
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Energon: Towards Efficient Acceleration of Transformers Using Dynamic Sparse Attention
TL;DR: In this article, an algorithm-architecture co-design approach that accelerates various transformers using dynamic sparse attention is proposed, which adopts low bitwidth in each filtering round and only uses high precision tensors in the attention stage to reduce overall complexity.
References
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Proceedings Article
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Proceedings Article
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TL;DR: This work presents a residual learning framework to ease the training of networks that are substantially deeper than those used previously, and provides comprehensive empirical evidence showing that these residual networks are easier to optimize, and can gain accuracy from considerably increased depth.
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