Lossless Sparse Temporal Coding for SNN-based Classification of Time-Continuous Signals

• Published in: 2023 Design, Automation & Test in Europe Conference & Exhibition (DATE) pp. 1 - 6
• Main Authors: Loh, Johnson, Gemmeke, Tobias
• Format: Conference Proceeding
• Language: English
• Published: EDAA 01.04.2023
• Subjects: ANN-SNN mapping; application-specific integrated circuit (ASIC); Artificial neural networks; Electrocardiography; lossless coding; Neurons; Quality of service; Real-time systems; Semiconductor device modeling; Silicon-on-insulator; sparse tem-poral coding
• ISSN: 1558-1101
• DOI: 10.23919/DATE56975.2023.10137112

Ultra-low power classification systems using spiking neural networks (SNN) promise efficient processing for mobile devices. Temporal coding represents activations in an artificial neural network (ANN) as binary signaling events in time, thereby minimizing circuit activity. Discrepancies in numeric results are inherent to common conversion schemes, as the atomic computing unit, i.e. the neuron, performs algorithmically different operations and, thus, potentially degrading SNN's quality of service (QoS). In this work, a lossless conversion method is derived in a top-down design approach for continuous time signals using electrocardiogram (ECG) classification as an example. As a result, the converted SNN achieves identical results compared to its fixed-point ANN reference. The computations, implied by proposed method, result in a novel hybrid neuron model located in between the integrate-and-fire (IF) and conventional ANN neuron, which numerical result is equivalent to the latter. Additionally, a dedicated SNN accelerator is implemented in 22 nm FDSOI CMOS suitable for continuous real-time classification. The direct comparison with an equivalent ANN counterpart shows that power reductions of 2.32\times and area reductions of 7.22\times are achievable without loss in QoS.