Computer Engineering seminars
September 2026
What Can Hardware Do for Computer Security?
Srini Devadas· Massachusetts Institute of Technology
Thu, Sep 24 · 23:00 UTC · Cambridge, Cambridge · Hybrid
Srini Devadas examines how hardware and cryptography can strengthen each other. Hardware can protect keys, monitor attacks in real time and reduce the trusted computing base, while hardware vulnerabilities can undermine software security. The talk presents research on accelerating cryptographic computation and using those capabilities to design more efficient protocols. It also asks how cryptography and new security definitions can guide the construction of secure hardware. Devadas is MIT's Webster Professor of Electrical Engineering and Computer Science. Hybrid: MIT Stata Center, Room 32-G449 (Kiva), 32 Vassar Street, Cambridge, MA 02139; online via Zoom. Thursday 24 September 2026, talk at 19:00 EDT (UTC−4); in-person refreshments begin at 18:30. This public event is organized by the Boston Chapter of the IEEE Computer Society and GBC/ACM. Register through the organizer-linked Zoom form for either mode and indicate online or in-person attendance. For physical access, use the Stata Center entrance nearest Main Street, then the fourth-floor elevators.
May 2026
Fault-Tolerant, Distributed In-Memory Computing for Large-Scale Linear Algebra and Optimization: An Algorithm–Hardware Co-Design Approach
Paritosh Ramanan· Oklahoma State University
Recorded
Paritosh Ramanan presents algorithm–hardware co-design for reliable linear algebra and optimization on resistive-memory in-memory computing systems. The distributed MELISO simulation framework supports multiple hardware models, while multilevel error correction makes noisy, low-energy devices useful for matrix-vector multiplication. Simulations report energy improvements of up to five orders of magnitude and latency reductions of up to two for high-dimensional linear algebra. A distributed primal-dual hybrid gradient solver for linear programs combines convergence analysis under device noise with simulated gains of up to two orders in latency and three in energy over GPU baselines on medium-scale problems. Preliminary randomized Kaczmarz results use online signal-to-noise estimates to select rows, comparing this strategy with offline alternatives. The talk closes with open problems in in-memory computation.
Video pageLinear AlgebraMatrix AlgebraSeries: Institute for Computational and Experimental Research in Mathematics (ICERM), Brown University
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