Suhail Basalama
Suhail Basalama

Ph.D. Candidate

About Me

Suhail Basalama is a Ph.D. student in Computer Science at the University of California, Los Angeles, working in the VLSI Architecture, Synthesis, and Technology (VAST) Lab. His research focuses on hardware acceleration for machine learning, custom and domain-specific computing, and high-level synthesis. He has developed novel methodologies and frameworks for optimizing systolic arrays, spacial accelerators, and dataflow architectures.

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Interests
  • Hardware acceleration for ML
  • Domain-specific computing
  • Electronic design automation (EDA)
  • VLSI design methodologies
  • Algorithm/hardware co-design
  • Hardware compilation and synthesis
  • High-level synthesis (HLS)
  • Reconfigurable computing
Education
  • Ph.D. in Computer Science

    University of California, Los Angeles

  • M.S. in Computer Science

    University of California, Los Angeles

  • B.S. in Computer Engineering

    University of Arkansas

  • B.A. in Political Science

    University of Arkansas

📚 My Research

I’m a Ph.D. researcher at UCLA’s VAST Lab, working at the intersection of hardware and machine learning. My work spans hardware-software co-design, high-level synthesis, and efficient accelerator architectures for ML workloads. I build compilers, frameworks, and design automation tools that push the limits of FPGA and ASIC performance.

Always happy to discuss research, exchange ideas, or collaborate! 😊

Publications
(2025). Stream-HLS: Towards Automatic Dataflow Acceleration. In FPGA'25 (Best Paper Candidate ★).
(2024). TAPA-CS: Enabling Scalable Accelerator Design on Distributed HBM-FPGAs. In ASPLOS'24.
(2023). FlexCNN: An end-to-end framework for composing CNN accelerators on FPGA. In TRETS'23.
(2023). Callipepla: Stream Centric Instruction Set and Mixed Precision for Accelerating Conjugate Gradient Solver. In FPGA'23.