76 citations · 120 across the 12 of their papers we have counts for
12 papers
From Human Guidance to Autonomy: Agent Skill System for End-to-End LLM Deployment on Spatial NPUs
Jiajie Li, Erwei Wang, Zhiru Zhang +1
Spatial neural processing units (NPUs) provide an energy-efficient platform for edge LLM inference, but efficiently deploying an LLM end-to-end on such hardware remains labor-inten…
AsyncSparse: Accelerating Sparse Matrix-Matrix Multiplication on Asynchronous GPU Architectures
Jie Liu, Huanzhi Pu, Zhiru Zhang
Sparse Matrix-Matrix Multiplication (SpMM) is a fundamental kernel across scientific computing and machine learning. While prior work accelerates SpMM using Tensor Cores, no existi…
From Pragmas to Partners: A Symbiotic Evolution of Agentic High-Level Synthesis
Niansong Zhang, Sunwoo Kim, Shreesha Srinath +1
The rise of large language models has sparked interest in AI-driven hardware design, raising the question: does high-level synthesis (HLS) still matter in the agentic era? We argue…
GauS: Differentiable Scheduling Optimization via Gaussian Reparameterization
Yaohui Cai, Vesal Bakhtazad, Cunxi Yu +1
Efficient operator scheduling is a fundamental challenge in software compilation and hardware synthesis. While recent differentiable approaches have sought to replace traditional o…
Magellan: Autonomous Discovery of Novel Compiler Optimization Heuristics with AlphaEvolve
Hongzheng Chen, Alexander Novikov, Ngân Vũ +5
Modern compilers rely on hand-crafted heuristics to guide optimization passes. These human-designed rules often struggle to adapt to the complexity of modern software and hardware…
Tawa: Automatic Warp Specialization for Modern GPUs with Asynchronous References
Hongzheng Chen, Bin Fan, Alexander Collins +8
Modern GPUs feature specialized hardware units that enable high-performance, asynchronous dataflow execution. However, the conventional SIMT programming model is fundamentally misa…
Dato: A Task-Based Programming Model for Dataflow Accelerators
Shihan Fang, Hongzheng Chen, Niansong Zhang +4
Recent deep learning workloads increasingly push computational demand beyond what current memory systems can sustain, with many kernels stalling on data movement rather than comput…
Characterizing and Optimizing Realistic Workloads on a Commercial Compute-in-SRAM Device
Niansong Zhang, Wenbo Zhu, Courtney Golden +4
Compute-in-SRAM architectures offer a promising approach to achieving higher performance and energy efficiency across a range of data-intensive applications. However, prior evaluat…
RapidStream IR: Infrastructure for FPGA High-Level Physical Synthesis
Jason Lau, Yuanlong Xiao, Yutong Xie +7
The increasing complexity of large-scale FPGA accelerators poses significant challenges in achieving high performance while maintaining design productivity. High-level synthesis (H…
Allo: A Programming Model for Composable Accelerator Design
Hongzheng Chen, Niansong Zhang, Shaojie Xiang +3
Special-purpose hardware accelerators are increasingly pivotal for sustaining performance improvements in emerging applications, especially as the benefits of technology scaling co…
Trainable Fixed-Point Quantization for Deep Learning Acceleration on FPGAs
Dingyi Dai, Yichi Zhang, Jiahao Zhang +4
Quantization is a crucial technique for deploying deep learning models on resource-constrained devices, such as embedded FPGAs. Prior efforts mostly focus on quantizing matrix mult…
Understanding the Potential of FPGA-Based Spatial Acceleration for Large Language Model Inference
Hongzheng Chen, Jiahao Zhang, Yixiao Du +5
Recent advancements in large language models (LLMs) boasting billions of parameters have generated a significant demand for efficient deployment in inference workloads. The majorit…