MSc thesis project proposal

[2023] Mixed-signal neuromorphic hardware for sub-10µW on-chip online learning at the edge

Neuromorphic hardware has recently been shown to enable end-to-end on-chip online learning over second-long timescales with the fully-digital ReckOn chip [1]. When the supply voltage is reduced to 0.5V, ReckOn only needs a power budget of 50µW to carry out on-chip training at the edge. While this is a record for on-chip training on temporal data, tiny autonomous sensor nodes powered by energy harvesting imply a maximum power budget of a few microwatts.

In this MSc project, we will investigate how mixed-signal design can help us further reduce the power budget of ReckOn. Indeed, while clock-based digital simulation requires state updates (and thus energy-costly memory accesses) at each integration timestep, sub-threshold analog design allows for a direct emulation of brain dynamics in real time [2]. However, the price to pay with analog design is an increase of area, as well as a higher susceptibility to noise, mismatch, and power-voltage-temperature (PVT) variations, and thus a reduction of the accuracy of the system. The goal of this MSc project is two-fold:
(i) based on a system-level power-accuracy-area evaluation, identify which computational primitives of ReckOn should be moved to the analog domain,
(ii) implement the resulting mixed-signal system, and demonstrate a new record for the power budget (ideally below 10µW).

[1] C. Frenkel, Charlotte and G. Indiveri, "ReckOn: A 28nm Sub-mm² Task-Agnostic Spiking Recurrent Neural Network Processor Enabling On-Chip Learning over Second-Long Timescales," IEEE International Solid-State Circuits Conference (ISSCC), 2022.
[2] G. Indiveri and S.-C. Liu, "Memory and information processing in neuromorphic systems." Proceedings of the IEEE, vol. 103, no. 8, pp. 1379-1397, 2015.


For this multi-disciplinary project, background in machine learning and digital design is required. A strong background in analog design is also necessary.
Previous experience with neuromorphic architectures is not expected.

Interested students should send a motivation letter together with their CV (incl. course transcripts and grades) to Dr. Charlotte Frenkel at

More MSc proposals for Dr. Charlotte Frenkel will appear in the coming weeks, interested students are encouraged to reach out by e-mail to enquire about upcoming projects.


dr. Charlotte Frenkel

Electronic Instrumentation Group

Department of Microelectronics

Last modified: 2022-11-30