← 研踪 PaperTrailarXiv:2610.05037 · v2
SparseCraft: Agentic Hardware-Software Co-Optimization for Sparse Computing
SparseCraft：面向稀疏计算的智能体软硬件协同优化
首次提交 2026-10-04 16:06
版本更新 2026-11-10 02:14
雷达观测 2026-11-10 15:22
摘要
Sparse-accelerator design spaces are usually searched against analytical models, so a design is judged by a model's prediction rather than by a measurement of the hardware. SparseCraft is a closed loop on the CHIA framework in which a large language model (LLM) agent co-designs the configuration, memories and sparse-kernel schedule of a Gemmini accelerator. A candidate counts only after it passes a legality check, cycle-accurate simulation, a bit-for-bit comparison of every output and synthesis. Post-synthesis timing analysis checks its clock, and its energy is computed from measured counters, with per-event energies validated against gate-level and SRAM-macro data. We compare the agent with expert Gemmini configurations and with random, greedy and multi-objective TPE search at the agent's budget of 15 evaluations, all measured by the same flow. On a GraphChallenge sparse-DNN layer, the agent's designs reach 159-164 the inverse energy-delay-area product of stock Gemmini and 2.07-2.14 that of the best expert configuration. Their Pareto hypervolume is 0.5557-0.5704, against at most 0.2806 for search. On a power-law graph and a pruned BERT layer, the agent raises perf/W by 11.45 and 4.43, and held-out transfer shows that its designs specialise to their workload. The loop is also resilient to faulty designs. When a design fails a check, a repair agent diagnoses the failure from its evidence and fixes it automatically, or declines when the only fix would undo the intended change. In a fault-injection benchmark, it fixed every scorable fault at its first attempt.
作者备注：Accepted at the A3 Workshop (Agentic Approaches to Architecture) co-located with MICRO 2026, Athens, Greece
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v2 · 2026-11-10 02:14 · 最新观测版本
SparseCraft: Agentic Hardware-Software Co-Optimization for Sparse Computing
Sparse-accelerator design spaces are usually searched against analytical models, so a design is judged by a model's prediction rather than by a measurement of the hardware. SparseCraft is a closed loop on the CHIA framework in which a large language model (LLM) agent co-designs the configuration, memories and sparse-kernel schedule of a Gemmini accelerator. A candidate counts only after it passes a legality check, cycle-accurate simulation, a bit-for-bit comparison of every output and synthesis. Post-synthesis timing analysis checks its clock, and its energy is computed from measured counters, with per-event energies validated against gate-level and SRAM-macro data. We compare the agent with expert Gemmini configurations and with random, greedy and multi-objective TPE search at the agent's budget of 15 evaluations, all measured by the same flow. On a GraphChallenge sparse-DNN layer, the agent's designs reach 159-164 the inverse energy-delay-area product of stock Gemmini and 2.07-2.14 that of the best expert configuration. Their Pareto hypervolume is 0.5557-0.5704, against at most 0.2806 for search. On a power-law graph and a pruned BERT layer, the agent raises perf/W by 11.45 and 4.43, and held-out transfer shows that its designs specialise to their workload. The loop is also resilient to faulty designs. When a design fails a check, a repair agent diagnoses the failure from its evidence and fixes it automatically, or declines when the only fix would undo the intended change. In a fault-injection benchmark, it fixed every scorable fault at its first attempt.
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