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Today’s machine learning (ML) systems, running workloads, such as deep neural networks, which require billions of parameters and many hours to train a model, consume a significant amount of energy. Due to the complexity of computation and topology, even the quantized models are hard to deploy on edge devices under energy constraints. To combat this, researchers have been focusing on new emerging neuromorphic computing models. Two of those models are hyperdimensional computing (HDC) and spiking neural networks (SNNs), both with their own benefits. HDC has various desirable properties that other ML algorithms lack, such as robustness to noise, simple operations, and high parallelism. SNNs are able to process event-based signal data in an efficient manner. This work develops $\mathsf {HyperSpike}$ , which utilizes a single, randomly initialized, and untrained SNN layer as a feature extractor connected to a trained HDC classifier. HDC is used to enable more efficient classification as well as provide robustness to errors. We experimentally show that $\mathsf {HyperSpike}$ is on average $31.5\times $ more robust to errors than traditional SNNs. On Intel’s Loihi (Davies et al., 2018), $\mathsf {HyperSpike}$ is $10\times $ faster and $2.6\times $ more energy efficient over traditional SNN networks. We further develop $\mathsf {HyperSpikeASIC}$ , a customized accelerator for $\mathsf {HyperSpike}$ . By decoupling the neuron and synapses, $\mathsf {HyperSpikeASIC}$ skips the inactive neurons and limits the neuron state updating to once per time step at most. $\mathsf {HyperSpikeASIC}$ is $601\times $ faster and $3467\times $ more energy efficient than $\mathsf {HyperSpike}$ running on Intel’s Loihi for SNN acceleration, and $12.2\times $ faster and $211\times $ more energy efficient than the state-of-the-art SNN ASIC implementation (Wang et al., 2022).

The brain is the perfect place to look for inspiration to develop more efficient neural networks. The inner workings of our synapses and neurons provide a glimpse at what the future of deep learning might look like. This article serves as a tutorial and perspective showing how to apply the lessons learned from several decades of research in deep learning, gradient descent, backpropagation, and neuroscience to biologically plausible spiking neural networks (SNNs). We also explore the delicate interplay between encoding data as spikes and the learning process; the challenges and solutions of applying gradient-based learning to SNNs; the subtle link between temporal backpropagation and spike timing-dependent plasticity; and how deep learning might move toward biologically plausible online learning. Some ideas are well accepted and commonly used among the neuromorphic engineering community, while others are presented or justified for the first time here. A series of companion interactive tutorials complementary to this article using our Python package, snnTorch, are also made available: https://snntorch.readthedocs.io/en/latest/tutorials/index.html.

Recent breakthroughs in neuromorphic computing show that local forms of gradient descent learning are compatible with Spiking Neural Networks (SNNs) and synaptic plasticity. Although SNNs can be scalably implemented using neuromorphic VLSI, an architecture that can learn using gradient-descent in situ is still missing. In this paper, we propose a local, gradient-based, error-triggered learning algorithm with online ternary weight updates. The proposed algorithm enables online training of multi-layer SNNs with memristive neuromorphic hardware showing a small loss in the performance compared with the state-of-the-art. We also propose a hardware architecture based on memristive crossbar arrays to perform the required vector-matrix multiplications. The necessary peripheral circuitry including presynaptic, post-synaptic and write circuits required for online training, have been designed in the subthreshold regime for power saving with a standard 180 nm CMOS process.

We present the Surrogate-gradient Online Error-triggered Learning (SOEL) system for online few-shot learning on neuromorphic processors. The SOEL learning system uses a combination of transfer learning and principles of computational neuroscience and deep learning. We show that partially trained deep Spiking Neural Networks (SNNs) implemented on neuromorphic hardware can rapidly adapt online to new classes of data within a domain. SOEL updates trigger when an error occurs, enabling faster learning with fewer updates. Using gesture recognition as a case study, we show SOEL can be used for online few-shot learning of new classes of pre-recorded gesture data and rapid online learning of new gestures from data streamed live from a Dynamic Active-pixel Vision Sensor to an Intel Loihi neuromorphic research processor.