[R] Stella Nera: Achieving 161 TOp/s/W with Multiplier-free DNN Acceleration based on Approximate Matrix Multiplication

**Paper**: [https://arxiv.org/abs/2311.10207](https://arxiv.org/abs/2311.10207)

**Code**: [https://github.com/joennlae/halutmatmul](https://github.com/joennlae/halutmatmul)

**Abstract**:

>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 …

abstract accelerator computing deep learning efficiency hash hpc indexing look lut machinelearning product quantization table