7 citations · 7 across the 9 of their papers we have counts for
7 papers · 1 filter
MemExplorer: Navigating the Heterogeneous Memory Design Space for Agentic Inference NPUs
Haoran Wu, Zeyu Cao, Yao Lai +16
Emerging agentic large language model (LLM) workloads are driving rapidly growing demand for memory capacity and bandwidth. Different phases of inference, such as prefill and decod…
KernelCraft: Benchmarking for Agentic Close-to-Metal Kernel Generation on Emerging Hardware
Jiayi Nie, Haoran Wu, Yao Lai +9
New AI accelerators with novel instruction set architectures (ISAs) often require developers to manually craft low-level kernels, a time-consuming and error-prone process that does…
Enhancing LUT-based Deep Neural Networks Inference through Architecture and Connectivity Optimization
Binglei Lou, Ruilin Wu, Philip Leong
Deploying deep neural networks (DNNs) on resource-constrained edge devices such as FPGAs requires a careful balance among latency, power, and hardware resource usage, while maintai…
NPU Design for Diffusion Language Model Inference
Binglei Lou, Haoran Wu, Kevin Lau +9
Diffusion-based LLMs (dLLMs) fundamentally depart from traditional autoregressive (AR) LLM inference: they leverage bidirectional attention, block-wise KV cache refreshing, cross-s…
Combating the Memory Walls: Optimization Pathways for Long-Context Agentic LLM Inference
Haoran Wu, Can Xiao, Jiayi Nie +15
LLMs now form the backbone of AI agents across a diverse range of applications, including tool use, command-line interfaces, and web or computer interaction. These agentic LLM infe…
SparseLUT: Sparse Connectivity Optimization for Lookup Table-based Deep Neural Networks
Binglei Lou, Ruilin Wu, Philip Leong
The deployment of deep neural networks (DNNs) on resource-constrained edge devices such as field-programmable gate arrays (FPGAs) requires a careful balance of latency, power, and…
fSEAD: a Composable FPGA-based Streaming Ensemble Anomaly Detection Library
Binglei Lou, David Boland, Philip H. W. Leong
Machine learning ensembles combine multiple base models to produce a more accurate output. They can be applied to a range of machine learning problems, including anomaly detection.…