Stella Nera: Achieving 161 TOp/s/W with Multiplier-free DNN Acceleration
  based on Approximate Matrix Multiplication
Jannis Schönleber Lukas Cavigelli Renzo Andri Matteo Perotti Luca Benini
From classical HPC to deep learning, MatMul is at the heart of today's
computing. The recent Maddness method approximates MatMul without the need for
multiplication by using a hash-based version of product quantization (PQ)
indexing into a look-up table (LUT). Stella Nera is the first Maddness
accelerator and it achieves 15x higher area efficiency (GMAC/s/mm^2) and more
than 25x higher energy efficiency (TMAC/s/W) than direct MatMul accelerators
implemented in the same technology. The hash function is a decision tree, which
allows for an efficient hardware implementation as the multiply-accumulate
operations are replaced by decision tree passes and LUT lookups. The entire
Maddness MatMul can be broken down into parts that allow an effective
implementation with small computing units and memories, allowing it to reach
extreme efficiency while remaining generically applicable for MatMul tasks. In
a commercial 14nm technology and scaled to 3nm, we achieve an energy efficiency
of 161 TOp/s/W@0.55V with a Top-1 accuracy on CIFAR-10 of more than 92.5% using
ResNet9.