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AB9: A neural processor for inference acceleration

Authors :
Hyun-Mi Kim
Jinho Han
Minseok Choi
Je-Seok Ham
Chan Kim
Kyoung-Seon Shin
Jeongmin Yang
Young-Su Kwon
Yong Cheol Peter Cho
Chun-Gi Lyuh
Jaehoon Chung
Source :
ETRI Journal, Vol 42, Iss 4, Pp 491-504 (2020)
Publication Year :
2020
Publisher :
Wiley, 2020.

Abstract

We present AB9, a neural processor for inference acceleration. AB9 consists of a systolic tensor core (STC) neural network accelerator designed to accelerate artificial intelligence applications by exploiting the data reuse and parallelism characteristics inherent in neural networks while providing fast access to large on‐chip memory. Complementing the hardware is an intuitive and user‐friendly development environment that includes a simulator and an implementation flow that provides a high degree of programmability with a short development time. Along with a 40‐TFLOP STC that includes 32k arithmetic units and over 36 MB of on‐chip SRAM, our baseline implementation of AB9 consists of a 1‐GHz quad‐core setup with other various industry‐standard peripheral intellectual properties. The acceleration performance and power efficiency were evaluated using YOLOv2, and the results show that AB9 has superior performance and power efficiency to that of a general‐purpose graphics processing unit implementation. AB9 has been taped out in the TSMC 28‐nm process with a chip size of 17 × 23 mm2. Delivery is expected later this year.

Details

ISSN :
22337326 and 12256463
Volume :
42
Database :
OpenAIRE
Journal :
ETRI Journal
Accession number :
edsair.doi.dedup.....9ef0057f1f8dfc07bd921b88950bac39
Full Text :
https://doi.org/10.4218/etrij.2020-0134