NeuroSoC concentrates on multiprocessor systems on chip with in-memory neural processing units.
- 2022 - 2026
- Nele Mentens
- Horizon Europe Program
STMicroelectronics / King's College, London / Panepistimio Patron / Universita Di Bologna / Ubotica Technologies Limited / Software Competence Center, Hagenberg / Benkei / IBM Research GMBH / Eidgenoessische Technische Hochschule, Zürich / Universiteit Leiden / Universita Degli Studi di Pavia / Thales / Robert Bosch GMBH
Deployment of intelligence at the edge presents many challenges because devices need to be low-cost and, as such, they are often constrained in computing capacity, memory, and energy resources. These constraints are not compatible with the need for much more advanced AI algorithms calling for Mbytes of storage and tens of GOPS per inference and call for leaner edge AI algorithms. The current state of the art for (the few) edge-AI chips relies on low-cost process technologies at 90 or 40nm and in some cases up to 16nm, with power efficiency between 1-5 TOPS/w and power densities up to 1 TOPS/mm2.
Recently several industrial projects and a few products have started to surface pursing neuromorphic and in memory computing, but none of these efforts have reached a level of maturity compatible with a mass volume production and cost, and, moreover the technology base they rely on is either not scalable to more advanced nodes (flash) or, targeting AI computing algorithms whose practical applications are yet to be fully proven (e.g., spiking). The NeuroSoC approach instead is to rely on a solid, mature, and qualified reliable Phase Change Memory technology to create an industrially proven path to go past the state of the art, as such, the NeuroSoC chip pre-product demonstration of the technology will be the first of his kind worldwide.
NeuroSoC’s aim is to develop an advanced Multi-Processor System on Chip prototype in FD-SOI 28nm CMOS technology that tightly integrates an AIMC IMNPU unit, a local digital processing subsystem, and functional safe multiprocessor host subsystems based on an enhanced version of existing RISC-V microprocessor implementation, while covering IMNPU security aspects holistically to tackle the requirements of a wide set of edge-AI applications.
The project will leverage STMicroelectronics’s unique high-density embedded PCM cell process technology being the denser and only such technology qualified and mature for embedded use in the industry world.