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The paper aims to analyze and optimize the performance of restricted AI accelerators like the CMP 170HX.

March 15, 2026Open Access

Instruction-Level Performance Analysis and Optimization Strategies for Constrained AI Accelerators: A Case Study of CMP 170HX

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The paper aims to analyze and optimize the performance of restricted AI accelerators like the CMP 170HX.
Developed an instruction-level performance analysis framework
Conducted experiments on CMP 170HX comparing it to unrestricted GPUs
Implemented operator extension based on PyTorch for various AI tasks
Achieved up to approximately 2× end-to-end inference performance improvement
Maintained numerical accuracy during optimizations
Provided practical insights for efficiency optimization of low-cost GPUs

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Instruction-level analysis shows optimization strategies enhance energy efficiency in AI accelerators, indicating significant performance gains.

Kangwei Xing (2026) studied this question.

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Instruction-level analysis shows optimization strategies enhance energy efficiency in AI accelerators, indicating significant performance gains.