Moving CNN Accelerator Computations Closer to Data
Sumanth Gudaparthi, Surya Narayanan, Rajeev Balasubramonian · 2018
A significant fraction of energy in recent CNN accelerators is dissipated in moving operands between storage and compute units. In this work, we re-purpose the CPU's last level cache to perform in-situ dot-product computations, thus significantly reducing data movement. Since a last level cache has several subarrays, many such dot-products can be performed in parallel, thus boosting throughput as well. The in-situ operation does not require analog circuits; it is performed with a bit-wise AND of two subarray rows, followed by digital aggregation of partial sums. The proposed architecture yields a 2.74× improvement in throughput and a 6.31× improvement in energy, relative to a DaDianNao baseline. This is primarily because the proposed architecture eliminates a large fraction of data transfers over H-Tree interconnects in the cache.