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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
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…
cs.DC2026
WISP: Waste- and Interference-Suppressed Distributed Speculative LLM Serving at the Edge via Dynamic Drafting and SLO-Aware Batching
Xiangchen Li, Jiakun Fan, Qingyuan Wang +7
As Large Language Models (LLMs) become increasingly accessible to end users, an ever-growing number of inference requests are initiated from edge devices and computed on centralize…