16 citations · 25 across the 5 of their papers we have counts for
5 papers
CHARM: Composing Heterogeneous Accelerators for Matrix Multiply on Versal ACAP Architecture
Jinming Zhuang, Jason Lau, Hanchen Ye +10
Dense matrix multiply (MM) serves as one of the most heavily used kernels in deep learning applications. To cope with the high computation demands of these applications, heterogene…
Compiler-Driven Simulation of Reconfigurable Hardware Accelerators
Zhijing Li, Yuwei Ye, Stephen Neuendorffer +1
As customized accelerator design has become increasingly popular to keep up with the demand for high performance computing, it poses challenges for modern simulator design to adapt…
ScaleHLS: A New Scalable High-Level Synthesis Framework on Multi-Level Intermediate Representation
Hanchen Ye, Cong Hao, Jianyi Cheng +4
High-level synthesis (HLS) has been widely adopted as it significantly improves the hardware design productivity and enables efficient design space exploration (DSE). Existing HLS…
Vyasa: A High-Performance Vectorizing Compiler for Tensor Convolutions on the Xilinx AI Engine
Prasanth Chatarasi, Stephen Neuendorffer, Samuel Bayliss +2
Xilinx's AI Engine is a recent industry example of energy-efficient vector processing that includes novel support for 2D SIMD datapaths and shuffle interconnection network. The cur…
Parallel Programming for FPGAs
Ryan Kastner, Janarbek Matai, Stephen Neuendorffer
This book focuses on the use of algorithmic high-level synthesis (HLS) to build application-specific FPGA systems. Our goal is to give the reader an appreciation of the process of…