Niansong Zhang

Hi there! I am an MS/PhD student at Cornell University. I work with Prof. Zhiru Zhang on Electronic Design Automation, Domain-Specific Language for hardware design, and hardware accelerators.

Department: Electrical & Computer Engineering
Lab: Computer Systems Laboratory
Office: 432 Rhodes Hall

Email  /  GitHub  /  LinkedIn

Blog Posts

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Education
Cornell University

M.S./Ph.D. in Electrical and Computer Engineering
Advisor: Prof. Zhiru Zhang
Aug 2021 - Present

Sun Yat-sen University

B.Eng. in Telecommunication Engineering
Advisor: Prof. Xiang Chen
Outstanding thesis
Aug 2016 - Jun 2020

Work Experience
Intel Labs

Exempt Tech Employee
Specification and Validation End-to-End (SAVE) Group, SCL/ADR/IL
Advisor: Jin Yang, Sunny Zhang
Feb 2021 - Aug 2021

Tsinghua University

Research Assistant
Nanoscale Integrated Circuits and System Lab (NICS-EFC)
Advisor: Prof. Yu Wang
Nov 2019 - Aug 2021

The University of Waterloo

MITACS Research Intern
WatCAG Group
Advisor: Prof. Nachiket Kapre
Jul 2019 - Oct 2019

Research & Publication

My research interests include EDA (Electronic Design Automation) for FPGA, DSL (Domain-Specific Language), and efficient machine learning.

Accelerator Design with Decoupled Hardware Customizations: Benefits and Challenges

Debjit Pal, Yi-Hsiang Lai, Shaojie Xiang, Niansong Zhang, Hongzheng Chen, Jeremy Casas, Pasquale Cocchini, Zhenkun Yang, Jin Yang, Louis-Noël Pouchet, Zhiru Zhang

DAC 2022 Invited Paper, to appear | paper

We first explain the advantages of the decoupled programming model and further discuss some of our recent efforts to enable a robust and viable verification solution in the future.

CodedVTR: Codebook-Based Sparse Voxel Transformer with Geometric Guidance

Tianchen Zhao, Niansong Zhang, Xuefei Ning, He Wang, Li Yi, Yu Wang

CVPR 2022, paper | website | slides | poster | video

We propose a flexible 3D Transformer on sparse voxels to address transformer's generalization issue. CodedVTR (Codebook-based Voxel TRansformer) decomposes attention space into linear combinations of learnable prototypes to regularize attention learning. We also propose geometry-aware self-attention to guide training with geometric pattern and voxel density.

HeteroFlow: An Accelerator Programming Model with Decoupled Data Placement for Software-Defined FPGAs

Shaojie Xiang, Yi-Hsiang Lai, Yuan Zhou, Hongzheng Chen, Niansong Zhang, Debjit Pal, Zhiru Zhang

FPGA 2022, paper | code

We propose an FPGA accelerator programming model that decouples the algorithm specification from optimizations related to orchestrating the placement of data across a customized memory hierarchy.

RapidLayout: Fast Hard Block Placement of FPGA-optimized Systolic Arrays using Evolutionary Algorithms

Niansong Zhang, Xiang Chen, Nachiket Kapre

TRETS 2022, paper

We extend the previous work on RapidLayout with cross-SLR routing, placement transfer learning, and placement bootstrapping from a much smaller device to improve runtime and design quality.

aw_nas: A Modularized and Extensible NAS Framework

Xuefei Ning, Changcheng Tang, Wenshuo Li, Songyi Yang, Tianchen Zhao, Niansong Zhang, Tianyi Lu, Shuang Liang, Huazhong Yang, Yu Wang

Arxiv Preprint, paper | code

We build an open-source Python framework implementing various NAS algorithms in a modularized and extensible manner.

RapidLayout: Fast Hard Block Placement of FPGA-optimized Systolic Arrays using Evolutionary Algorithms

Niansong Zhang, Xiang Chen, Nachiket Kapre

FPL 2020, paper | code

Michal Servit Best Paper Award Nominee

We build a fast and high-performance evolutionary placer for FPGA-optimized hard block designs that targets high clock frequency such as 650+MHz.

Workshops

Enabling Fast Deployment and Efficient Scheduling for Multi-Node and Multi-Tenant DNN Accelerators in the Cloud

Shulin Zeng, Guohao Dai, Niansong Zhang, Yu Wang

MICRO 2021 ASCMD Workshop, paper | video

We propose a multi-node and multi-core accelerator architecture and a decoupled compiler for cloud-backed INFerence-as-a-Service (INFaaS).

Professional Services

Student Volunteer: FCCM’22

Awards and Honors

DAC Young Fellow 2021

FPL 2020 Best paper Nomination (Michal Servit Award)

Outstanding Bachelor Thesis Award | Sun Yat-sen University

Mitacs Globalink Research Internship Award | Mitacs, Canada

First-class Merit Scholarship x2 | Sun Yat-sen University

Lin and Liu Foundation Scholarship | SEIT, Sun Yat-sen University

Patents

Niansong Zhang, Songyi Yang, Shun Fu, Xiang Chen, "Industry Profile Geometric Dimension Automatic Measuring Method Based on Computer Vision Imaging." Chinese Patent CN201811539019.8A, filed December 17, 2018, and issued April 19, 2019.

Niansong Zhang (at Novauto Technology), "A Pruning Method and Device of Multi-task Neural Network Models", Chinese Patent 202010805327.1, filed August 12, 2020.

Last updated: May 24, 2022

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