NextGArch Lab
Pioneering the Future of NextGen Systems and Networks!
At the University of Michigan, NextGArch Lab is dedicated to revolutionizing computer systems, networks, and architectures through cutting-edge research and innovation in CSE and EECS.
About NextGArch Lab
Our Mission
At the NextGArch Lab, we conduct innovative research in computer systems, networks, and architectures. Our work involves developing domain-specific abstractions, compilers, and architectures for networks and systems, with applications in AI/ML, self-driving networks, cloud/edge computing, and 5G/6G.
Research Areas
Domain-Specific Systems
New programming models, runtime systems, and architectures for nextgen high-performance and scalable computing.
Domain-Specific Networks
Novel networking protocols, architectures, and algorithms for efficient and reliable data communication.
Domain-Specific Architectures
Innovative architectures for future computing systems, including hw/sw co-design and accelerators for line-rate and proficient ML/AI.
Systems+X and Networks+X
Leveraging cross-domain insights—e.g., X = ML/AI—to push the boundaries of efficiency, scalability, and innovation in distributed and networked systems.
Latest News
July 14, 2025
NetSparse accepted to MICRO '25. Congratulations, Gerasimos Gerogiannis, Dimitrios Merkouriadis, Charles Block, Annus Zulfiqar, and the team!

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Sparse workloads are everywhere—from ML to scientific computing—but scaling them across datacenter nodes hits a hard wall: network bottlenecks. | Muhammad Shahbaz

Sparse workloads are everywhere—from ML to scientific computing—but scaling them across datacenter nodes hits a hard wall: network bottlenecks. 🧱🧱🧱 Enter NetSparse: a new network architecture designed to supercharge distributed sparse computations. By pushing communication logic into NICs and switches, NetSparse slashes traffic, eliminates redundancy, and brings us dramatically closer to ideal scaling! 🚀🚀🚀 Huge congrats to Gerasimos Gerogiannis for leading the effort and my amazing co-au

July 14, 2025
Ali Imran presented our GigaFlow project at the MEDS Lab, University of Engineering and Technology (UET)!

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🧠 𝗙𝗿𝗼𝗺 𝗧𝗿𝗮𝘃𝗲𝗿𝘀𝗮𝗹𝘀 𝘁𝗼 𝗧𝗿𝗮𝗶𝗻𝗶𝗻𝗴 — 𝗔 𝗗𝗲𝗲𝗽 𝗗𝗶𝘃𝗲 𝘄𝗶𝘁𝗵 𝗔𝗹𝗶 𝗜𝗺𝗿𝗮𝗻 🚀💬 | Maktab-e-Digital Systems

🧠 𝗙𝗿𝗼𝗺 𝗧𝗿𝗮𝘃𝗲𝗿𝘀𝗮𝗹𝘀 𝘁𝗼 𝗧𝗿𝗮𝗶𝗻𝗶𝗻𝗴 — 𝗔 𝗗𝗲𝗲𝗽 𝗗𝗶𝘃𝗲 𝘄𝗶𝘁𝗵 𝗔𝗹𝗶 𝗜𝗺𝗿𝗮𝗻 🚀💬 We had the privilege of welcoming one of 𝗠𝗘𝗗𝗦 𝗟𝗮𝗯’𝘀 𝗲𝗮𝗿𝗹𝗶𝗲𝘀𝘁 𝘁𝗿𝗮𝗶𝗻𝗶𝗻𝗴 𝗺𝗮𝗻𝗮𝗴𝗲𝗿𝘀, Ali Imran, for an engaging and insight-packed session — reconnecting with the lab and sharing the cutting-edge research he's been involved in since. Ali walked us through a spectrum of cutting-edge topics — from 𝘀𝗺𝗮𝗿𝘁𝗹𝘆 𝗽𝗮𝗿𝘁𝗶𝘁𝗶𝗼𝗻𝗶𝗻𝗴 𝘁𝗿𝗮𝘃𝗲𝗿𝘀𝗮𝗹 𝗮

July 3, 2025
Enkeleda Bardhi gave an inspiring talk at Euro S&P on how collaborative DDoS detection strategies, powered by programmable data planes, can lead to impressive gains. Congratulations, Enkeleda!
July 2nd, 2025
Woot! Ali Imran is heading to AMD this fall for an internship to uncover the hidden workings of this new workload, we call LLM, using their GPUs and ML stack. Congrats, Ali Imran!

www.amd.com

AMD Research

May 29, 2025
Prof. Shahbaz gave an invited talk during the IA382: Seminar in Computer Engineering at FEEC/UNICAMP!

feec-seminar-comp-eng.github.io

Fast, Flexible, and Intelligent Next-Generation Networks and Systems

Abstract # Maintaining strict security and performance objectives in next-generation cloud and edge networks demands that compute-intensive management and control decisions are made on the current state of the entire network (e.

May 9, 2025
SpliDT accepted to SRC TECHCON '25. Many congratulations to Marilyn Rego (Undergraduate Lead), Murayyiam Parvez, Annus Zulfiqar, and the team!

www.src.org

TECHCON 2025 (Event E007206) - SRC

Join us for TECHCON 2025, the premier conference for innovation and excellence in the semiconductor industry! Taking place September 7-10, 2025, at the Renaissance Hotel in Austin, Texas, this exclusive, members-only event brings together industry leaders, researchers, students, and recruiters to explore cutting-edge advancements, exchange ideas, and build the future of microelectronics.

May 8, 2025
Gigaflow and SpliDT accepted into this year’s Google Summer of Code (GSoC) cohort. Congratulations to Advay Singh and Sankalp Jha for their successful proposals and for leading the projects this summer!

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📢🎉 Thrilled to share that NextGArch Lab’s projects, Gigaflow and SpliDT,… | Muhammad Shahbaz

📢🎉 Thrilled to share that NextGArch Lab’s projects, Gigaflow and SpliDT, have been accepted into this year’s Google Summer of Code (GSoC) cohort! The projects will be led by Advay Singh, a rising undergraduate in Computer Science and Engineering at the University of Michigan, and Sankalp Jha from AJAY KUMAR GARG ENGINEERING COLLEGE, GHAZIABAD—supported by an incredible team of mentors from Google and NextGArch Lab! NextGArch Lab: https://lnkd.in/geqB2Xyq Gigaflow: https://lnkd.in/gEvn5WE9 Go

Apr 23, 2025
Many congratulations to Annus Zulfiqar on being inducted into Sigma Xi, The Scientific Research Honor Society!

www.sigmaxi.org

Home

Top awards were given by judges and public vote for the 2025 virtual student competition.

Our Team
Muhammad Shahbaz
Principal Investigator (PI)
Assistant Professor, U-M (CSE)
Ertza Warraich
Ph.D. Student (Purdue)
Focus: ML Systems and Networks, Security, and AI
Annus Zulfiqar
Ph.D. Student (U-M)
Focus: HW/SW Co-Design, Virtual Networks, and In-Network AI
Bilal Saleem
Ph.D. Student (Purdue)
Focus: Cloud-Native Systems, Edge Computing, and 5G
Murayyiam Parvez
Ph.D. Student (Purdue)
Focus: Network Security, In-Network ML, and Programmable Data Planes
Ali Imran
Ph.D. Student (U-M)
Focus: Programmable Data Planes, In-Network ML, and Agentic Systems
Marilyn Rego
Ph.D. Student (U-M)
Focus: Agentic Systems, In-Network ML, and Domain-Specific LLMs
Qizheng Zhang
Ph.D. Student (Stanford), co-advised with Kunle Olukotun
Focus: ML and Agentic Systems, In-Network ML, and Video Streaming
Omar Basit
Ph.D. Student (Purdue), co-advised with Y. Charlie Hu
Focus: AR/VR, 5G Systems and Cloud/Edge Computing
Venkat Kunaparaju
B.S./M.S. Student (Purdue)
Focus: HW/SW Co-Design, Virtual Networks, and Cloud Computing
Advay Singh
B.S. Student (U-M)
Focus: Cloud Computing, and ML Systems and Networks
Regan McDonald
B.S. Student (U-M)
Focus: Networking and Systems
Shina Patel
B.E. Student (U-M)
Focus: Embedded Systems and Networks
Sophia Chen
B.E. Student (U-M)
Focus: In-Network ML, and Domain-Specific LLMs
Selected Publications
MICRO '25
NetSparse: In-Network Acceleration of Distributed Sparse Kernels
Gerasimos Gerogiannis, Charles Block, Dimitrios Merkouriadis, Annus Zulfiqar, Filippos Tofalos, Muhammad Shahbaz, Josep Torrellas
TECHCON '25
SpliDT: Partitioned Decision Trees for Scalable Stateful ML Inference at Line Rate
Marilyn Rego, Murayyiam Parvez, Annus Zulfiqar, Roman Beltiukov, Shir Landau Feibish, Walter Willinger, Arpit Gupta, Muhammad Shahbaz
Euro S&P '25
O'MINE: A Novel Collaborative DDoS Detection Mechanism for Programmable Data-Planes
Enkeleda Bardhi, Chenxing Ji, Ali Imran, Muhammad Shahbaz, Riccardo Lazzeretti, Mauro Conti, Fernando Kuipers
ISCA '25
HardHarvest: Hardware-Supported Core Harvesting for Microservices
Jovan Stojkovic, Chunao Liu, Muhammad Shahbaz, Josep Torrellas
ASPLOS '25
Gigaflow: Pipeline-Aware Sub-Traversal Caching for Modern SmartNICs
Annus Zulfiqar, Ali Imran, Venkat Kunaparaju, Ben Pfaff, Gianni Antichi, Muhammad Shahbaz

Computer Science and Engineering

Streamlining cloud traffic with a Gigaflow Cache

Gigaflow improves virtual switches for programmable SmartNICs, delivering a 51% higher hit rate and 90% lower misses.

Tech Xplore

Gigaflow cache streamlines cloud traffic, with 51% higher hit rate and 90% lower misses for programmable SmartNICs

A new way to temporarily store memory, Gigaflow, helps direct heavy traffic in cloud data centers caused by AI and machine learning workloads, according to a study led by University of Michigan researchers.

15:34

YouTube

Gigaflow - Pipeline-Aware Sub-Traversal Caching for Modern SmartNICs (ASPLOS 2025)

Learn about Gigaflow: a high hit rate, SmartNIC-native cache for virtual switches (like OVS) that expands rule space coverage by two orders of magnitude and reduces cache misses by up to 90%. This work was presented as ASPLOS'25.

NSDI '25
OptiReduce: Resilient and Tail-Optimal AllReduce for Distributed Deep Learning in the Cloud
Ertza Warraich, Omer Shabtai, Khalid Manaa, Shay Vargaftik, Yonatan Piasetzky, Matty Kadosh, Lalith Suresh, Muhammad Shahbaz
Google Research Scholar Award

Computer Science and Engineering

Perfect is the enemy of good for distributed deep learning in the cloud

Leveraging deep learning’s resilience, approximating data lost by allowing some servers to time out speeds up model training while preserving performance.

Tech Xplore

Perfect is the enemy of good for distributed deep learning in the cloud

A new communication-collective system, OptiReduce, speeds up AI and machine learning training across multiple cloud servers by setting time boundaries rather than waiting for every server to catch up, ...

Our Sponsors
NSF
National Science Foundation
SRC
Semiconductor Research Corporation
Intel
Intel Corporation
Google Research
Google
Facebook
Meta
VMware Research
by Broadcom
AMD
Advanced Micro Devices
Nvidia
Nvidia Corporation
ONF
Open Networking Foundation
Contact Us
University of Michigan
Computer Science and Engineering, EECS
Location
Leinweber Computer Science and Information Building (4252), University of Michigan
Social Media
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