A 28nm 27.5TOPS/W Approximate-Computing-Based Transformer Processor with Asymptotic Sparsity Speculating and Out-of-Order Computing

Author:

Wang Yang1,Qin Yubin1,Deng Dazheng1,Wei Jingchuan1,Zhou Yang1,Fan Yuanqi1,Chen Tianbao2,Sun Hao1,Liu Leibo1,Wei Shaojun1,Yin Shouyi1

Affiliation:

1. Tsinghua University,Beijing,China

2. Tsing Micro,Beijing,China

Funder

NSFC

National Key Research and Development Program

Beijing Innovation Center for Future Chip

BAAl

Publisher

IEEE

Reference6 articles.

1. An 8.93 TOPS/W LSTM Recurrent Neural Network Accelerator Featuring Hierarchical Coarse-Grain Sparsity for On-Device Speech Recognition

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3. NVIDIA A100 Tensor Core GPU Architecture;nVidia Whitepaper,0

4. A 25mm2 SoC for loT Devices with 18ms Noise-Robust Speech-to-Text Latency via Bayesian Speech Denoising and Attention-Based Sequence-to-Sequence DNN Speech Recognition in 16nm FinFET;tambe;ISSCC,2021

5. A3: Accelerating Attention Mechanisms in Neural Networks with Approximation;ham;IEEE HPCA,2020

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