1 citations · 3 across the 9 of their papers we have counts for
5 papers · 1 filter
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…
Polymorph: Energy-Efficient Multi-Label Classification for Video Streams on Embedded Devices
Saeid Ghafouri, Mohsen Fayyaz, Xiangchen Li +4
Real-time multi-label video classification on embedded devices is constrained by limited compute and energy budgets. Yet, video streams exhibit structural properties such as label…
QPART: Adaptive Model Quantization and Dynamic Workload Balancing for Accuracy-aware Edge Inference
Xiangchen Li, Saeid Ghafouri, Bo Ji +3
As machine learning inferences increasingly move to edge devices, adapting to diverse computational capabilities, hardware, and memory constraints becomes more critical. Instead of…
SLED: A Speculative LLM Decoding Framework for Efficient Edge Serving
Xiangchen Li, Dimitrios Spatharakis, Saeid Ghafouri +5
The growing gap between the increasing complexity of large language models (LLMs) and the limited computational budgets of edge devices poses a key challenge for efficient on-devic…
APEX: Asynchronous Parallel CPU-GPU Execution for Online LLM Inference on Constrained GPUs
Jiakun Fan, Yanglin Zhang, Xiangchen Li +1
Deploying large language models (LLMs) for online inference is often constrained by limited GPU memory, particularly due to the growing KV cache during auto-regressive decoding. Hy…