From classical HPC to deep learning,Matrix Multiplication (MatMul) is at the heart of today’s computing.
The recentMaddness111Multiply-ADDitioN-lESSmethod approximatesMatMulwithout the need for multiplication by using a hash-based version of product quantization (PQ)  indexing into a look-up table (Look-Up Table (LUT) ) .Stella Nera222Named after an album of the famous Swiss musicians Patent Ochsneris the firstMaddnessaccelerator and it achieves 15x higher area efficiency (GMAC/s/mm2)  and a 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 andLUTlookups. The entireMaddnessMatMul 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 14 nm technology and scaled to 3 nm, 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.