# tubGEMM: Energy-Efficient and Sparsity-Effective Temporal-Unary-Binary Based Matrix Multiply Unit

arXiv: https://arxiv.org/abs/2412.17955 · HTML: https://mlsanity.com/paper/2412.17955/

## Key facts

- Cited by 2 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 23 Dec 2024.

## Built on by (most cited first, 2 of 2)

- [Tempus Core: Area-Power Efficient Temporal-Unary Convolution Core for Low-Precision Edge DLAs](https://mlsanity.com/paper/2412.19002/) (2024)
- [Exploration of Unary Arithmetic-Based Matrix Multiply Units for Low Precision DL Accelerators](https://mlsanity.com/paper/2602.00838/) (2026)

## Abstract (arXiv)

General Matrix Multiplication (GEMM) is a ubiquitous compute kernel in deep learning (DL). To support energy-efficient edge-native processing, new GEMM hardware units have been proposed that operate on unary encoded bitstreams using much simpler hardware. Most unary approaches thus far focus on rate-based unary encoding of values and perform stochastic approximate computation. This work presents tubGEMM, a novel matrix-multiply unit design that employs hybrid temporal-unary and binary (tub) encoding and performs exact (not approximate) GEMM. It intrinsically exploits dynamic value sparsity to improve energy efficiency. Compared to the current best unary design uGEMM, tubGEMM significantly reduces area, power, and energy by 89%, 87%, and 50%, respectively. A tubGEMM design performing 128x128 matrix multiply on 8-bit integers, in commercial TSMC N5 (5nm) process node, consumes just 0.22 mm2 die area, 417.72 mW power, and 8.86 uJ energy, assuming no sparsity. Typical sparsity in DL workloads (MobileNetv2, ResNet-50) reduces energy by more than 3x, and lowering precision to 4 and 2 bits further reduces it by 24x and 104x respectively.