Mixture of Cache-Conditional Experts for Efficient Mobile Device Inference

• URL: http://arxiv.org/abs/2412.00099v1
• Date: Wed, 27 Nov 2024 18:59:48 GMT
• Title: Mixture of Cache-Conditional Experts for Efficient Mobile Device Inference
• Authors: Andrii Skliar, Ties van Rozendaal, Romain Lepert, Todor Boinovski, Mart van Baalen, Markus Nagel, Paul Whatmough, Babak Ehteshami Bejnordi,
• Abstract summary: We introduce a novel cache-aware routing strategy that leverages expert reuse during token generation to improve cache locality.<n>We present on-device results demonstrating 2$times$ speedups on mobile devices.
• Score: 14.57414071160821
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: Mixture of Experts (MoE) LLMs have recently gained attention for their ability to enhance performance by selectively engaging specialized subnetworks or "experts" for each input. However, deploying MoEs on memory-constrained devices remains challenging, particularly when generating tokens sequentially with a batch size of one, as opposed to typical high-throughput settings involving long sequences or large batches. In this work, we optimize MoE on memory-constrained devices where only a subset of expert weights fit in DRAM. We introduce a novel cache-aware routing strategy that leverages expert reuse during token generation to improve cache locality. We evaluate our approach on language modeling, MMLU, and GSM8K benchmarks and present on-device results demonstrating 2$\times$ speedups on mobile devices, offering a flexible, training-free solution to extend MoE's applicability across real-world applications.

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