activity
20232026
most citedUnderstanding the Potential of FPGA-Based Spatial Acceleration for Large Language Model Inference

76 citations · 116 across the 6 of their papers we have counts for

collaborators

6 papers

cs.CL2026

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…

cs.CL2025

From Loop Nests to Silicon: Mapping AI Workloads onto AMD NPUs with MLIR-AIR

Erwei Wang, Samuel Bayliss, Andra Bisca +19

General-purpose compilers abstract away parallelism, locality, and synchronization, limiting their effectiveness on modern spatial architectures. As modern computing architectures…

cs.PL2025

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…

cs.AR2025

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…

cs.PL2024★ 40 cited

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…

cs.LG2023★ 76 cited

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…