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We present a new back propagation based training algorithm for discrete-time spiking neural networks (SNN). Inspired by recent deep learning algorithms on binarized neural networks, binary activation with a straight-through gradient estimator is used to model the leaky integrate-fire spiking neuron, overcoming the difficulty in training SNNs using back propagation. Two SNN training algorithms are proposed: (1) SNN with discontinuous integration, which is suitable for rate-coded input spikes, and (2) SNN with continuous integration, which is more general and can handle input spikes with temporal information. Neuromorphic hardware designed in 28nm CMOS exploits the spike sparsity and demonstrates high classification accuracy (>98% on MNIST) and low energy (51.4–773 nJ/image).
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Enhancing SNN Training Performance: A Mixed-Signal Triplet Reconfigurable STDP Circuit with Multiplexing Encoding
In spike-timing-dependent plasticity (STDP), synap-tic weights are modified according to the relative time difference between pre and post-synaptic spikes of spiking neural network (SNN). A triplet STDP model was proposed since this model can better take account of a series of spikes and thus more closely mimic the activity in biological neural systems. Circuit that can switch between different STDP rules was also introduced to improve the range of STDP applications. To apply the advantages of triplet STDP to various tasks, a mixed-signal triplet reconfigurable STDP circuit and its hardware prototype are proposed in this paper. The performance analysis of the STDP training algorithm is carried out with a hardware testbench as well as Pytorch-based SNN. This triplet STDP design achieves 3.28% and 3.63% higher accuracy than the pair STDP learning rule through datasets such as MNIST and CIFAR-10. Our design shows one of the best reconfigurability while keeping a relatively low energy per spike operation (SOP) through the performance comparison with the state of the arts.
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- PAR ID:
- 10439791
- Date Published:
- Journal Name:
- 2023 IEEE International Symposium on Circuits and Systems (ISCAS)
- Page Range / eLocation ID:
- 1 to 5
- Format(s):
- Medium: X
- Sponsoring Org:
- National Science Foundation
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Spiking neural network (SNN) has attracted more and more research attention due to its event-based property. SNNs are more power efficient with such property than a conventional artificial neural network. For transferring the information to spikes, SNNs need an encoding process. With the temporal encoding schemes, SNN can extract the temporal patterns from the original information. A more advanced encoding scheme is a multiplexing temporal encoding which combines several encoding schemes with different timescales to have a larger information density and dynamic range. After that, the spike timing dependence plasticity (STDP) learning algorithm is utilized for training the SNN since the SNN can not be trained with regular training algorithms like backpropagation. In this work, a spiking domain feature extraction neural network with temporal multiplexing encoding is designed on EAGLE and fabricated on the PCB board. The testbench’s power consumption is 400mW. From the test result, a conclusion can be drawn that the network on PCB can transfer the input information to multiplexing temporal encoded spikes and then utilize the spikes to adjust the synaptic weight voltage.more » « less
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Abstract Programmable synaptic devices that can achieve timing‐dependent weight updates are key components to implementing energy‐efficient spiking neural networks (SNNs). Electrochemical ionic synapses (EIS) enable the programming of weight updates with very low energy consumption and low variability. Here, the strongly nonlinear kinetics of EIS, arising from nonlinear dynamics of ions and charge transfer reactions in solids, are leveraged to implement various forms of spike‐timing‐dependent plasticity (STDP). In particular, protons are used as the working ion. Different forms of the STDP function are deterministically predicted and emulated by a linear superposition of appropriately designed pre‐ and post‐synaptic neuron signals. Heterogeneous STDP is also demonstrated within the array to capture different learning rules in the same system. STDP timescales are controllable, ranging from milliseconds to nanoseconds. The STDP resulting from EIS has lower variability than other hardware STDP implementations, due to the deterministic and uniform insertion of charge in the tunable channel material. The results indicate that the ion and charge transfer dynamics in EIS can enable bio‐plausible synapses for SNN hardware with high energy efficiency, reliability, and throughput.more » « less
- Free Publicly Accessible Full Text
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Accepted Manuscript1.0
- Conference Paper:
- https://doi.org/10.1109/ISCAS46773.2023.10181729
