A High-Throughput Neural Network Accelerator

Tianshi Chen, Zidong Du, Ninghui Sun, Jia Wang, Chengyong Wu, Yunji Chen, Olivier Temam

Abstract: The authors designed an accelerator architecture for large-scale neural networks, with an emphasis on the impact of memory on accelerator design, performance, and energy. In this article, they present a concrete design at 65 nm that can perform 496 16-bit fixed-point operations in parallel every 1.02 ns, that is, 452 gop/s, in a 3.02mm <sup xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">2</sup> , 485-mw footprint (excluding main memory accesses).