Abstract:
A versatile reconfigurable accelerator for binary/ternary deep neural networks (DNNs) is presented. It features a massively parallel in-memory processing architecture and...Show MoreMetadata
Abstract:
A versatile reconfigurable accelerator for binary/ternary deep neural networks (DNNs) is presented. It features a massively parallel in-memory processing architecture and stores varieties of binary/ternary DNNs with a maximum of 13 layers, 4.2 K neurons, and 0.8 M synapses on chip. The 0.6 W, 1.4 TOPS chip achieves performance and energy efficiency that is 10–102 and 102–104 times better than a CPU/GPU/FPGA.
Published in: 2017 Symposium on VLSI Circuits
Date of Conference: 05-08 June 2017
Date Added to IEEE Xplore: 14 August 2017
ISBN Information: