6 papers · 1 filter
FPGN: Redefining Ultra-Fast Programmable Gate-based Neural Acceleration with Differentiable LUTs
Jiawei Liang, Haotong Qin, Linfeng Du +7
Achieving nanosecond-scale inference latency for deep neural networks (DNNs) has become a primary architectural concern for latency-critical applications. While Field-Programmable…
AutoINV: Automated Invariant Generation Framework for Formal Verification on High-Level Synthesis Designs
Xiaofeng Zhou, Linfeng Du, Guangyu Hu +3
High-level synthesis (HLS) transforms an algorithmic description of hardware from a higher abstraction (e.g., C/C++) into a register-transfer level (RTL) design, offering reduced d…
FPPS: An FPGA-Based Point Cloud Processing System
Xiaofeng Zhou, Linfeng Du, Hanwei Fan +1
Point cloud processing is a computational bottleneck in autonomous driving systems, especially for real-time applications, while energy efficiency remains a critical system constra…
FLEX: Leveraging FPGA-CPU Synergy for Mixed-Cell-Height Legalization Acceleration
Xingyu Liu, Jiawei Liang, Linfeng Du +5
In this work, we present FLEX, an FPGA-CPU accelerator for mixed-cell-height legalization tasks. We address challenges from the following perspectives. First, we optimize the task…
DRACO: Co-design for DSP-Efficient Rigid Body Dynamics Accelerator
Xingyu Liu, Jiawei Liang, Yipu Zhang +5
We propose a hardware-efficient RBD accelerator based on FPGA, introducing three key innovations. First, we propose a precision-aware quantization framework that reduces DSP demand…
Fast and Practical Strassen's Matrix Multiplication using FPGAs
Afzal Ahmad, Linfeng Du, Wei Zhang
Matrix multiplication is a cornerstone operation in a wide array of scientific fields, including machine learning and computer graphics. The standard algorithm for matrix multiplic…