An RL agent using Soft Actor-Critic with Mixture-of-Experts jointly optimizes ASIC architecture, memory hierarchy, and partitioning for AI inference, achieving 29809 tokens/s for Llama 3.1 at 3nm and under 13mW for SmolVLM across 3-28nm nodes without manual retuning.
Placeto: Learn- ing generalizable device placement algo- rithms for distributed machine learning
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From LLM to Silicon: RL-Driven ASIC Architecture Exploration for On-Device AI Inference
An RL agent using Soft Actor-Critic with Mixture-of-Experts jointly optimizes ASIC architecture, memory hierarchy, and partitioning for AI inference, achieving 29809 tokens/s for Llama 3.1 at 3nm and under 13mW for SmolVLM across 3-28nm nodes without manual retuning.