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.

Yael Parag proposes degrees of isolation and connection as a framework for analysing energy systems, rather than a binary distinction between isolated islands and connected grids. The framework links infrastructure design, governance, operating practices, resource dependence and collective perceptions across spacecraft, community systems, microgrids, islands and countries. Comparative cases examine how security, independence, climate goals, costs, technical constraints, ideology and geopolitics shape these arrangements and change over time. Montserrat illustrates both vulnerabilities and opportunities, including coordinated design of water, sewage and energy microgrids. The lecture also considers how regional exchange, cooperation and shared identity influence resilience, treating energy islands as evolving configurations within wider social and geographical settings.

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.

John Clarke, Michel H. Devoret and John M. Martinis trace the discovery that a macroscopic electrical circuit can display quantum tunnelling and discrete energy levels. Clarke connects early superconducting measuring devices with experiments on current-biased Josephson junctions. Microwave-induced resonances and the crossover from thermal activation to temperature-independent escape provide tests of quantum behaviour. Devoret explains how superconducting circuits became controllable artificial atoms, with Josephson elements enabling engineered energy spectra, qubits and quantum-limited amplifiers. He considers what these designed systems make possible beyond experiments with natural atoms. Martinis follows the development from early junction experiments to superconducting quantum processors. He discusses energy-level quantization, tunnelling, photon generation, quantum computational experiments, and the fabrication and error-control challenges involved in building useful machines.

Felice Manganiello introduces network coding, in which intermediate nodes combine incoming packets algebraically before forwarding them. This can improve communication efficiency, latency, and resilience to packet loss and congestion. The tutorial develops theoretical foundations and practical applications, including linear network coding, multicast communication, and coding across networks.

The use of electronic circuits to model neural systems goes back to C. Mead and is present in models, from leaky-integrate-and-fire to Hodking-Huxley. Simulating neural network with analog hardware is attractive: it allows to implement neurocomputations in real time without discretization approximations, it has perfect simulation-time scaling with system size, and it provides ready-to-deploy neuromorphic circuit for applications. There are implementations in CMOS technology, however, they are complex, require sophisticated fabrication facilities and, most important, suffer from significant device mismatch. In a radically different approach, based on the concept of memristors, we introduce a neuro-synaptic circuit of unprecedented simplicity, with readily available cheap off-the-shelf electronic components, that can quantitatively reproduce textbook theoretical neuron and synaptic current models. Our neuron circuits can avoid the mismatch problem and are easily tuneable at bio-compatible time-scales. We first introduce a voltage-gated conductance bursting neuron model that produces spike traces that bare striking similarity to experimental recordings. We then introduce synaptic current circuits and show the modularity of our method implementing neurocomputing primitives of basic network motifs, including CPGs. With this "theoretical hardware" approach we show: (i) that neuron adaptation and self-excitation can be viewed as a self-consistent dynamical problem; (ii) that a dynamical memory can be minimally implemented with a single recursive spiking neuron; (iii) that an adaptive membrane current reveals a connection between bursting and the driven harmonic oscillator, perhaps pointing to a neural correlate of the pendular limb motion. Finally we discuss the limitation of the approach to networks of mid-size and its potential application for brain-machine-interfaces, robotics and AI. Presented in the van Vreeswijk Theoretical Neuroscience Seminar series (formerly WWTNS) on 2025-03-19. Recording duration: 00:43:46.