Electrical Engineering seminars

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.

Michel Devoret retraces the experimental discovery of macroscopic quantum tunnelling and quantised energy levels in superconducting electrical circuits. The lecture explains how Josephson circuits became controllable quantum systems and how these experiments opened a route from observing quantum behaviour in a macroscopic device to engineering artificial atoms. He connects this foundational work, recognised by the 2025 Nobel Prize in Physics shared with John Clarke and John Martinis, to the development of superconducting qubits and present approaches to quantum computing. The account links the original physical phenomena to the subsequent effort to build useful quantum processors from electrical circuits.

The story of event cameras starts from the very beginnings of neuromorphic engineering with Misha Mahowald and Carver Mead. The chip design of these “silicon retina” cameras is the most crucial aspect that might enable them to come to mass production and widespread use. Once we have a usable camera is just the beginning, because now we need to think of our use of the data as though we were some type of artificial “silicon cortex”. That step has just started but the last few years have brought some remarkable results from the computer vision community. This talk will have a lot of live demonstrations.

Michel Devoret explains why a quantum measurement amplifier must transmit a signal without returning its own noise to the system being measured. He relates directional amplification to the breakdown of reciprocity, introducing Faraday rotation and microwave circulators used between superconducting circuits and measurement amplifiers. The lecture then examines the limitations of magnetic circulators: stray magnetic fields are undesirable near superconducting quantum circuits, and bulky components constrain the number of measurement channels inside a dilution refrigerator. Devoret develops a theoretical route to non-reciprocal microwave components using non-degenerate three-wave mixing in a frequency-conversion regime. The pump phase supplies an active analogue of the magnetic bias in the Faraday effect, allowing directionality to be engineered through phase differences.