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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 10–10 2 and 10 2 –10 4 times better than a CPU/GPU/FPGA.
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Kota Ando
Hokkaido University
Kodai Ueyoshi
KU Leuven
Kentaro Orimo
Hokkaido University
Hokkaido University
Tokyo Institute of Technology
Keio University
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Ando et al. (Thu,) studied this question.
synapsesocial.com/papers/6a11e6d0f7bd4f5c7da585d3 — DOI: https://doi.org/10.23919/vlsic.2017.8008533