Area-Efficient In-Memory Computing for Mixture-of-Experts via Multiplexing and Caching

TL;DR

This paper introduces an area-efficient in-memory computing architecture for Mixture-of-Experts transformers, leveraging multiplexing, expert grouping, and caching to significantly improve area, performance, and energy efficiency on process-in-memory hardware.

Contribution

It proposes a novel multiplexing and caching strategy that reduces area overhead and enhances efficiency for MoE transformers on PIM architectures.

Findings

Area efficiency improved by up to 2.2x over state-of-the-art.

Performance and energy efficiency during generation increased by 4.2x and 10.1x.

Total performance density reached 15.6 GOPS/W/mm2.

Abstract

Mixture-of-Experts (MoE) layers activate a subset of model weights, dubbed experts, to improve model performance. MoE is particularly promising for deployment on process-in-memory (PIM) architectures, because PIM can naturally fit experts separately and provide great benefits for energy efficiency. However, PIM chips often suffer from large area overhead, especially in the peripheral circuits. In this paper, we propose an area-efficient in-memory computing architecture for MoE transformers. First, to reduce area, we propose a crossbar-level multiplexing strategy that exploits MoE sparsity: experts are deployed on crossbars and multiple crossbars share the same peripheral circuits. Second, we propose expert grouping and group-wise scheduling methods to alleviate the load imbalance and contention overhead caused by sharing. In addition, to address the problem that the expert choice router requires access to all hidden states during generation, we propose a gate-output (GO)cache to store necessary results and bypass expensive additional computation. Experiments show that our approaches improve the area efficiency of the MoE part by up to 2.2x compared to a SOTA architecture. During generation, the cache improves performance and energy efficiency by 4.2x and 10.1x, respectively, compared to the baseline when generating 8 tokens. The total performance density achieves 15.6 GOPS/W/mm2. The code is open source at https://github.com/superstarghy/MoEwithPIM.