We are pleased to share that our collaborative research with NAIST, “Efficient Kernel Mapping and Comprehensive System Evaluation of LLM Acceleration on a CGLA” has been formally accepted to the international journal IEEE Access. You can access the full article for download here.
This work represents the first end-to-end evaluation of Large Language Model (LLM) inference on a non-AI-specialized Coarse-Grained Linear Array (CGLA) accelerator, using the state-of-the-art Qwen3 model family as the benchmark and reinforces the viability of general-purpose CGLA architectures—not just fixed-function ASICs or high-power GPUs—for next-generation LLM inference. It demonstrates that compute efficiency, programmability, and adaptability to changing algorithms can coexist in a reconfigurable architecture.
For LENZO, this is a meaningful milestone in advancing the underlying theory and validation behind our CGLA-based compute vision.
Publication Details
Title:
Efficient Kernel Mapping and Comprehensive System Evaluation of LLM Acceleration on a CGLA
Journal: IEEE Access
DOI: 10.1109/ACCESS.2025.3636266