# A Soft SIMD Based Energy Efficient Computing Microarchitecture

arXiv: https://arxiv.org/abs/2212.09358 · HTML: https://mlsanity.com/paper/2212.09358/

## Key facts

- No citing papers in the map yet (data through 14 Oct 2026).
- Builds on 4 of its 17 references that are in the map.
- First posted on arXiv 19 Dec 2022.

## Builds on (most cited first, 4 of 4)

- [Deep Learning for Financial Applications : A Survey](https://mlsanity.com/paper/2002.05786/) (2020)
- [Machine Learning at the Network Edge: A Survey](https://mlsanity.com/paper/1908.00080/) (2019)
- [Secure and Robust Machine Learning for Healthcare: A Survey](https://mlsanity.com/paper/2001.08103/) (2020)
- [Transform Quantization for CNN (Convolutional Neural Network) Compression](https://mlsanity.com/paper/2009.01174/) (2020)

## Abstract (arXiv)

The ever-increasing size and computational complexity of today's machine-learning algorithms pose an increasing strain on the underlying hardware. In this light, novel and dedicated architectural solutions are required to optimize energy efficiency by leveraging opportunities (such as intrinsic parallelism and robustness to quantization errors) exposed by algorithms. We herein address this challenge by introducing a flexible two-stages computing pipeline. The pipeline can support fine-grained operand quantization through software-supported Single Instruction Multiple Data (SIMD) operations. Moreover, it can efficiently execute sequential multiplications over SIMD sub-words thanks to zero-skipping and Canonical Signed Digit (CSD) coding. Finally, a lightweight repacking unit allows changing the bitwidth of sub-words at run-time dynamically. These features are implemented within a tight energy and area budget. Indeed, experimental results showcase that our approach greatly outperforms traditional hardware SIMD ones both in terms of area and energy requirements. In particular, our pipeline occupies up to 53.1% smaller than a hardware SIMD one supporting the same sub-word widths, while performing multiplication up to 88.8% more efficiently.