2024 BrainChip Discussion, page-8401

Neuromorphic Split Computing with Wake-Up
Radios: Architecture and Design via Digital
Twinning
Jiechen Chen, Member, IEEE, Sangwoo Park, Member, IEEE, Petar Popovski, Fellow, IEEE, H. Vincent Poor,
Life Fellow, IEEE, Osvaldo Simeone, Fellow, IEEE
Abstract—Neuromorphic computing leverages the sparsity of temporal data to reduce processing energy by activating a small subset of neurons and synapses at each time step. When deployed for split computing in edge-based systems, remote neuromorphic processing units (NPUs) can reduce the communication power budget by communicating asynchronously using sparse impulse radio (IR) waveforms. This way, the input signal sparsity trans- lates directly into energy savings both in terms of computation and communication. However, with IR transmission, the main contributor to the overall energy consumption remains the power required to maintain the main radio on. This work proposes a novel architecture that integrates a wake-up radio mechanism within a split computing system consisting of remote, wirelessly connected, NPUs. A key challenge in the design of a wake-up radio-based neuromorphic split computing system is the selection of thresholds for sensing, wake-up signal detection, and decision making. To address this problem, as a second contribution, this work proposes a novel methodology that leverages the use of a digital twin (DT), i.e., a simulator, of the physical system, coupled with a sequential statistical testing approach known as Learn Then Test (LTT) to provide theoretical reliability guarantees. The proposed DT-LTT methodology is broadly applicable to other design problems, and is showcased here for neuromorphic communications. Experimental results validate the design and the analysis, confirming the theoretical reliability guarantees and illustrating trade-offs among reliability, energy consumption, and informativeness of the decisions.
Index Terms—Neuromorphic computing, spiking neural net- works, wake-up radios, neuromorphic wireless communications, reliability.
I. INTRODUCTION
A. Context and Motivation
Neuromorphic processing units (NPUs), such as Intel’s Loihi or BrainChip’s Akida, leverage the sparsity of temporal data to reduce processing energy by activating a small subset
J. Chen, S. Park, and O. Simeone are with the King’s Communications, Learning and Information Processing (KCLIP) lab within the Centre for Intel- ligent Information Processing Systems (CIIPS) at the Department of Engineer- ing, King’s College London, London, WC2R 2LS, UK (email:{jiechen.chen, sangwoo.park, osvaldo.simeone}@kcl.ac.uk). O. Simeone is also with the De- partment of Electronic Systems, Aalborg University, 9100 Aalborg, Denmark. P. Popovski is with the Department of Electronic Systems, Aalborg University, 9100 Aalborg, Denmark (email: [email protected]). H. Vincent Poor is with the Department of Electrical and Computer Engineering, Princeton University, Princeton, NJ 08544 USA (e-mail:[email protected]).
This work was supported by the European Union’s Horizon Europe project CENTRIC (101096379), by an Open Fellowship of the EPSRC (EP/W024101/1), by the EPSRC project (EP/X011852/1), by Project REA- SON, a UK Government funded project under the Future Open Networks Research Challenge (FONRC) sponsored by the Department of Science Inno- vation and Technology (DSIT