Regan McDonald, Marilyn Rego, Ertza Warraich, Annus Zulfiqar, Muhammad Shahbaz
mrLLM: Fast Multi-Region LLM Inference using Learned Adaptors
Marilyn Rego, Maxwell Kumbong, Hermann Kumbong, Ertza Warraich, Muhammad Shahbaz
SpliDT: Partitioned Decision Trees for Scalable Stateful Inference at Line Rate
Murayyiam Parvez*, Annus Zulfiqar*, Roman Beltiukov, Shir Landau Feibish, Walter Willinger, Arpit Gupta, Muhammad Shahbaz (*co-primary)
33:26
YouTube
SpliDT: Partitioned Decision Trees for Scalable Stateful Inference at Line Rate || P4 Developer Day
Machine learning is increasingly used in programmable data planes, such as switches and smartNICs, to enable real-time traffic analysis and security monitoring at line rate. Decision trees (DTs) are particularly well-suited for these tasks due to their interpretability and compatibility with the Reconfigurable Match-Action Table (RMT) architecture. However, current DT implementations require collecting all features upfront, which limits scalability and accuracy due to constrained data plane reso
Reimagining RDMA Through the Lens of ML
Ertza Warraich, Ali Imran, Annus Zulfiqar, Shay Vargaftik, Sonia Fahmy, Muhammad Shahbaz
Gigaflow: Pipeline-Aware Caching in Virtual Switches with P4
Advay Singh, Annus Zulfiqar, Ali Imran, Muhammad Shahbaz
NetSparse: In-Network Acceleration of Distributed Sparse Kernels
Gerasimos Gerogiannis, Charles Block, Dimitrios Merkouriadis, Annus Zulfiqar, Filippos Tofalos, Muhammad Shahbaz, Josep Torrellas
SpliDT: Partitioned Decision Trees for Scalable Stateful Inference at Line Rate
Murayyiam Parvez, Annus Zulfiqar, Roman Beltiukov, Shir Landau Feibish, Walter Willinger, Arpit Gupta, Muhammad Shahbaz
Kairo: Incremental View Maintenance for Scalable Virtual Switch Caching
Annus Zulfiqar, Ben Pfaff, Gianni Antichi, Muhammad Shahbaz
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
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
HardHarvest: Hardware-Supported Core Harvesting for Microservices
Jovan Stojkovic, Chunao Liu, Muhammad Shahbaz, Josep Torrellas
Gigaflow: Pipeline-Aware Sub-Traversal Caching for Modern SmartNICs
Annus Zulfiqar, Ali Imran, Venkat Kunaparaju, Ben Pfaff, Gianni Antichi, Muhammad Shahbaz
p4.org
Gigaflow: Pipeline-Aware Sub-Traversal Caching for Modern SmartNICs – P4 – Language Consortium
Figure 1: (a) A traversal is a complete sequence of table lookups through the vSwitch pipeline that generates a Megaflow rule. (b) A sub-traversal is a subset of these lookups within a traversal, capturing smaller, reusable segments shared across multiple flows.
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.
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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.
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
A Smart Cache for a SmartNIC!
Annus Zulfiqar, Ali Imran, Venkat Kunaparaju, Ben Pfaff, Gianni Antichi, Muhammad Shahbaz
BranchPipe: Scalable Decision Trees for Stateful Processing at Line Rate
Murayyiam Parvez, Annus Zulfiqar, Roman Beltiukov, Shir Landau Feibish, Walter Willinger, Arpit Gupta, Muhammad Shahbaz
Caravan: Practical Online Learning of In-Network ML Models with Labeling Agents
Qizheng Zhang, Ali Imran, Enkeleda Bardhi, Tushar Swamy, Muhammad Shahbaz, Kunle Olukotun
EdgeScaler: Smart (Auto-)Scaling for the 5G Edge
Lauren Trinks (Lead Undergraduate Student), Bilal Saleem, Muhammad Shahbaz
GigaFlow: A Scalable and Efficient Hardware Fast-Path for Open vSwitch
Venkat Kunaparaju, Annus Zulfiqar, Ali Imran, Ben Pfaff, Gianni Antichi, Muhammad Shahbaz
A Smart Cache for a SmartNIC!
Annus Zulfiqar, Ali Imran, Venkat Kunaparaju, Ben Pfaff, Gianni Antichi, Muhammad Shahbaz
Caravan: Practical Online Learning of In-Network ML Models with Labeling Agents
Qizheng Zhang, Ali Imran, Enkeleda Bardhi, Tushar Swamy, Nathan Zhang, Muhammad Shahbaz, Kunle Olukotun
SRC JUMP 2.0 Best Paper Award
Modeling and Generating Control-Plane Traffic for Cellular Networks
Jiayi Meng, Jinqgi Huang, Y. Charlie Hu, Yaron Koral, Xiaojun Lin, Muhammad Shahbaz, Abhigyan Sharma
Best Paper Award (Runner Up)
Towards a Performant and Scalable Cloud-Native 5G Mobile Core Architecture
Jinqgi Huang*, Jiayi Meng*, Bilal Saleem*, Iftekhar Alam, Ajay Thakur, Muhammad Shahbaz, Christian Maciocco, Y. Charlie Hu (*co-primary)
The Slow Path Needs an Accelerator Too!
Annus Zulfiqar, Ben Pfaff, William Tu, Gianni Antichi, Muhammad Shahbaz
μManycore: A Cloud-Native CPU for Tail at Scale
Jovan Stojkovic, Chunao Liu, Muhammad Shahbaz, Josep Torrellas
IEEE Micro Top Picks (Honorable Mention)
Homunculus: Auto-Generating Efficient Data-Plane ML Pipelines for Datacenter Networks
Tushar Swamy, Annus Zulfiqar, Muhammad Shahbaz, Luigi Nardi, Kunle Olukotun
ASPLOS Distinguished Artifact Award
Hardware Support for Efficient and Secure Resource Harvesting in the Cloud
Jovan Stojkovic, Chunao Liu, Muhammad Shahbaz, Josep Torrellas
Enabling the Reflex Plane with the nanoPU
Stephen Ibanez, Alex Mallery, Serhat Arslan, Theo Jepsen, Muhammad Shahbaz, Changhoon Kim, Nick McKeown
The Case for Native Multi-Node In-Network Machine Learning
Lorenzo Bracciale, Tushar Swamy, Muhammad Shahbaz, Pierpaolo Loreti, Stefano Salsano, Hesham Elbakoury
Ultima: Robust and Tail-Optimal All-Reduce for Distributed Deep Learning
Ertza Warraich, Leonard Liu, Omer Shabtai, Yonatan Piasetzky, Shay Vargaftik, Matty Kadosh, Lalith Suresh, Muhammad Shahbaz
Accelerating 5G (Mobile Core) Control Plane using P4
Jingqi Huang*, Jiayi Meng*, Iftekhar Alam, Christian Maciocco, Y. Charlie Hu, Muhammad Shahbaz (*co-primary)
Primitives for Finite Field Arithmetic in Network Switches
Daniel Seara, Bernardo Conde, Eduard Marin, Muhammad Shahbaz, Muriel Medard, Fernando Ramos
PMNet: In-Network Data Persistence
Korakit Seemakhupt, Sihang Liu, Yasas Senevirathne, Muhammad Shahbaz, Samira Khan
Taurus: A Data Plane Architecture for Per-Packet ML
Tushar Swamy, Alexander Rucker, Muhammad Shahbaz, Ishan Gaur, Kunle Olukotun
IETF/IRTF ANRP Prize
IEEE Micro Top Picks (Honorable Mention)
Chopping Off the Tail: Bounded Non-Determinism for Real-Time Accelerators
Alexander Rucker, Muhammad Shahbaz, Kunle Olukotun
Best of CAL Paper Award
Facebook Research Award
Constructing the Face of Network Data
Ertza Warraich, Muhammad Shahbaz
The nanoPU: A Nanosecond RPC Stack for Data Centers
Stephen Ibanez, Alex Mallery, Serhat Arslan, Theo Jepsen, Muhammad Shahbaz, Changhoon Kim, Nick McKeown
Google Faculty Award
SARA: Scaling a Reconfigurable Dataflow Accelerator
Yaqi Zhang, Nathan Zhang, Tian Zhao, Matt Vilim, Muhammad Shahbaz, Kunle Olukotun
PMNet: In-Network Data Persistence
Korakit Seemakhupt, Sihang Liu, Yasas Senevirathne, Muhammad Shahbaz, Samira Khan
Towards Network-Efficient Cross-Regional Inference via Learned Activation Compression