3 papers
cs.AR2026
A Framework for Accelerating Transformer Inference on RISC-V for Edge AI
Ajay Kumar M, Vishnu PS, Yike Li +5
This work presents a framework for accelerating transformer-based language models (LMs) on resource-constrained IoT devices. The framework targets compact LMs: BERT-Tiny (B-Ty), Mo…
cs.AR2026
AdaptViT: Runtime-Adaptive Vision Transformer Deployment on Custom RISC-V
Vishnu PS, Ajay Kumar M, Yike Li +2
Deploying Vision Transformers (ViTs) on low-power edge devices is challenging due to high computational demands. Conventional pruning frameworks require a separate compiled binary…
cs.AR2026
ZTA-Q: an Open-source RISC-V Platform for Accurate Quantized CNN Inference
Yike Li, Ajay Kumar M, Vishnu PS +4
Low-precision inference is widely adopted in edge AI to reduce computational cost and memory footprint. However, existing open-source accelerator platforms provide limited end-to-e…