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.

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.

Spiking recurrent neural networks (RNNs) are a promising tool for solving a wide variety of complex cognitive and motor tasks, due to their rich temporal dynamics and sparse processing. However training spiking RNNs on dedicated neuromorphic hardware is still an open challenge. This is due mainly to the lack of local, hardware-friendly learning mechanisms that can solve the temporal credit assignment problem and ensure stable network dynamics, even when the weight resolution is limited. These challenges are further accentuated, if one resorts to using memristive devices for in-memory computing to resolve the von-Neumann bottleneck problem, at the expense of a substantial increase in variability in both the computation and the working memory of the spiking RNNs. In this talk, I will present our recent work where we introduced a PyTorch simulation framework of memristive crossbar arrays that enables accurate investigation of such challenges. I will show that recently proposed e-prop learning rule can be used to train spiking RNNs whose weights are emulated in the presented simulation framework. Although e-prop locally approximates the ideal synaptic updates, it is difficult to implement the updates on the memristive substrate due to substantial device non-idealities. I will mention several widely adapted weight update schemes that primarily aim to cope with these device non-idealities and demonstrate that accumulating gradients can enable online and efficient training of spiking RNN on memristive substrates.

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.