Niansong Zhang

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

Department: Electrical & Computer Engineering
Lab: Computer Systems Laboratory
Office: 471D Rhodes Hall

Email  /  GitHub  /  LinkedIn  /  Google Scholar

Blog Posts

News

🏅 Our FPGA 2024 paper HLS Formal Verification won the Best Paper Award. [News] (03/05/24)

🎊 Our journal paper RapidLayout won the Best Paper Award for ACM TRETS in 2023. [Award Plaque] (07/13/23)

Donated my hair to Locks of Love to support children suffering from hair loss. [Certificate] (07/04/23)

Our journal paper has been selected as the Featured Paper of IEEE Trans. on Computers. (05/11/23)

Our patent CN109658402B has been granted. [Certificate] [Google Patents] (04/18/23)

Selected as DAC Young Fellow [Certificate] (04/08/23)

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

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

Research Intern
Design Automation Research
Mentor/Manager: Anthony Agnesina, Mark Ren
May 2024 — Aug 2024

Compiler Intern
Advanced Compilers for Distribution and Computation (ACDC)
Chief Technology Organization (CTO)
Manager: Stephen Neuendorffer
May 2023 — Aug 2023

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

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

MITACS Research Intern
WatCAG
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.

Allo: A Programming Model for Composable Accelerator Design

Hongzheng Chen* Niansong Zhang*, Shaojie Xiang, Zhichen Zeng, Mengjia Dai, Zhiru Zhang

* Equal Contribution

Special-purpose hardware accelerators are vital for performance improvements, but current tools are inadequate for complex designs. Allo, a new composable programming model, decouples hardware customizations from algorithms and outperforms existing tools, showing significant improvements in benchmarks and deep learning models like GPT-2.

Formal Verification of Source-to-Source Transformations for HLS

Louis-Noël Pouchet, Emily Tucker, Niansong Zhang, Hongzheng Chen, Debjit Pal, Gaberiel Rodríguez, Zhiru Zhang

Best Paper Award

We target the problem of efficiently checking the semantics equivalence between two programs written in C/C++ as a means to ensuring the correctness of the description provided to the HLS toolchain, by proving an optimized code version fully preserves the semantics of the unoptimized one.

Supporting a Virtual Vector Instruction Set on a Commercial Compute-in-SRAM Accelerator

Courtney Golden, Dan Ilan, Caroline Huang, Niansong Zhang, Zhiru Zhang, Christopher Batten

We implement a virtual vector instruction set on a commercial Compute-in-SRAM device, and perform detailed instruction microbenchmarking to identify performance benefits and overheads.

Serving Multi-DNN Workloads on FPGAs: a Coordinated Architecture, Scheduling, and Mapping Perspective

Shulin Zeng, Guohao Dai, Niansong Zhang, Xinhao Yang, Haoyu Zhang, Zhenhua Zhu, Huazhong Yang, Yu Wang

Featured Paper in the May 2023 Issue

This paper proposes a Design Space Exploration framework to jointly optimize heterogeneous multi-core architecture, layer scheduling, and compiler mapping for serving DNN workloads on cloud FPGAs.

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

We show 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

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

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

ACM TRETS Best Paper Award

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

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

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.

An MLIR-based Intermediate Representation for Accelerator Design with Decoupled Customizations

Hongzheng Chen*, Niansong Zhang*, Shaojie Xiang, Zhiru Zhang

We decouple hardware customizations from the algorithm specifications at the IR level to: (1) provide a general platform for high-level DSLs, (2) boost performance and productivity, and (3) make customization verification scalable.

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

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

Reviewer: ICCAD 2022, 2023, TRETS, TCAS II

Teaching

[ECE 2300] Digital Logic and Computer Organization

Head TA, Spring 2024

[ECE 5775] High-Level Digital Design Automation

Part-time TA, Fall 2022

Awards and Honors

Best Paper Award for FPGA 2024

Best Paper Award for ACM TRETS in 2023

DAC Young Fellow 2021 & 2023

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.