2 citations · 5 across the 3 of their papers we have counts for
6 papers
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…
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…
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…
MARVEL: An End-to-End Framework for Generating Model-Class Aware Custom RISC-V Extensions for Lightweight AI
Ajay Kumar M, Cian O'Mahoney, Pedro Kreutz Werle +5
Deploying deep neural networks (DNNs) on resource-constrained IoT devices remains a challenging problem, often requiring hardware modifications tailored to individual AI models. Ex…
Bare-Metal RISC-V + NVDLA SoC for Efficient Deep Learning Inference
Vineet Kumar, Ajay Kumar M, Yike Li +2
This paper presents a novel System-on-Chip (SoC) architecture for accelerating complex deep learning models for edge computing applications through a combination of hardware and so…
KWT-Tiny: RISC-V Accelerated, Embedded Keyword Spotting Transformer
Aness Al-Qawlaq, Ajay Kumar M, Deepu John
This paper explores the adaptation of Transformerbased models for edge devices through the quantisation and hardware acceleration of the ARM Keyword Transformer (KWT) model on a RI…