Introduction
On the road toward artificial general intelligence (AGI), two solution paths have
been explored: neuroscience-driven neuromorphic computing such as spiking neural networks
(SNNs) and computer-science-driven machine learning such as artificial neural networks
(ANNs). Owing to availability of data, high-performance processors, effective learning
algorithms, and easy-to-use programming tools, ANNs have achieved tremendous breakthroughs
in many intelligent applications. Recently, SNNs also attracted a lot of attention
due to its biological plausibility and the possibility of achieving energy-efficiency
(Roy et al., 2019). However, they suffer from ongoing debates and skepticisms due
to worse accuracy compared to “standard” ANNs. The performance gap comes from a variety
of factors, including learning techniques, benchmarks, programming tools and execution
hardware, all of which in SNNs are not as developed as those in the ANN domain.
To this end, we propose a Research Topic, named “Understanding and Bridging the Gap
between Neuromorphic Computing and Machine Learning,” in Frontiers in Neuroscience
and Frontiers in Computational Neuroscience to collect recent researches on neuromorphic
computing and machine learning to help understand and bridge the aforementioned gap.
We received 18 submissions in total and accepted 14 of them in the end. The scope
of these accepted papers covers learning algorithms, applications, and efficient hardware.
Learning Algorithms
How to train SNN models is the key to improve its functionality, thus bridging the
gap between ANN models. Unlike the ANN domain that has grown rapidly via sophisticated
backpropagation-based learning algorithms, the SNN domain is still short of effective
learning algorithms due to the complicated spatiotemporal dynamics and non-differentiable
spike activities. Currently, there are overall two categories of learning algorithms
for SNNs: unsupervised synaptic plasticity with biological plausibility [e.g., spike
timing dependent plasticity, STDP (Diehl and Cook, 2015)] and supervised backpropagation
with gradient descent [e.g., indirect learning by acquiring gradients from the ANN
counterpart (Diehl et al., 2015; Sengupta et al., 2019), direct learning by acquiring
gradients from the SNN itself (Lee et al., 2016; Wu et al., 2018; Gu et al., 2019;
Zheng et al., 2021), or the combination of both (Rathi et al., 2020)]. The latter
can usually achieve higher accuracy and has advanced the model scale to handle ImageNet-level
tasks. In the future, we look forward to seeing more studies on SNN learning to close
the gap.
Next, we briefly summarize the recent progress of neural network (especially SNN)
learning presented in our accepted papers. Inspired by the curiosity-based learning
mechanism of the brain, Shi et al. propose curiosity-based SNN (CBSNN) models. In
the first training epoch, the novelty estimations of all samples are obtained through
bio-plausible synaptic plasticity; next, the samples whose novelty estimations exceed
the threshold are repeatedly learned and the novelty estimations are updated in the
next five epochs; then, all samples are learned with one more epoch. The last two
steps are periodically taken until convergence. CBSNNs show better accuracy and higher
efficiency in processing several small-scale datasets than conventional voltage-driven
plasticity-centric SNNs. Daram et al. propose ModNet, an efficient dynamic learning
system inspired from the neuromodulatory mechanism in the insect brain. An inbuilt
modulatory unit regulates learning based on the context and internal state of the
system. The network with modulatory trace achieves 98.8% ± 1.16 on average over the
omniglot dataset for five-way one-shot image classification task while using 20x fewer
trainable parameters compared to state-of-the-art models. Kaiser, Mostafa et al. introduce
deep continuous local learning (DECOLLE), an SNN model equipped with local error functions
for online learning. The synaptic plasticity rules are derived from user-defined cost
functions and neural dynamics by leveraging existing autodifferentiation methods of
machine learning frameworks. The model demonstrates state-of-the-art performance on
N-MNIST and DvsGesture datasets. Fang et al. propose a bio-plausible noise structure
to optimize the performance of SNNs trained by gradient descent. Through deducing
the strict saddle condition for synaptic plasticity, they demonstrate that the noise
helps the optimization escape from saddle points on high dimensional domains. The
accuracy improvement can reach at least 10% on MNIST and CIFAR10 datasets. Panda et
al. modify the SNN configuration with backward residual connections, stochastic softmax,
and hybrid artificial-and-spiking neuronal activations. In this way, the previous
learning methods are improved with comparable accuracy but large efficiency gains
over the ANN counterparts.
Applications
Unlike the artificial neuron in ANNs, each spiking neuron in SNNs has intrinsic temporal
dynamics, which is appropriate for processing sequence information. In this Research
Topic, we accepted two papers that discuss SNN applications. Wu et al. explore the
first work that uses SNNs for large-vocabulary continuous automatic speech recognition
(LVCSR) tasks. Their SNNs demonstrate competitive accuracies on par with their ANN
counterparts while consuming only 10 algorithmic timesteps and 0.68× total synaptic
operations. They integrate the models into the PyTorch-Kaldi Speech Recognition Toolkit
for rapid development. Kugele et al. apply SNNs for processing spatiotemporal event
streams (e.g., N-MNIST, CIFAR10-DVS, N-CARS, and DvsGesture datasets). They improve
the ANN-to-SNN conversion learning method by introducing connection delays during
the pre-training of ANNs to match the propagation delays in converted SNNs. In this
way, the resulting SNNs can handle the above tasks accurately and efficiently.
In addition, besides energy-efficiency (Merolla et al., 2014), recent studies further
find that the event-driven computing paradigm of SNNs endows them high robustness
(He et al., 2020; Liang et al., 2020) and superior capability in learning sparse features
(He et al., 2020). We believe it is very important to mine the true advantages of
SNNs to determine their true value in practical applications.
Efficient Hardware
Performing neural networks on general-purpose processors is inefficient, which stimulates
the development of various domain-specific hardware platforms, including those for
ANNs [e.g., DaDianNao (Chen et al., 2014), TPU (Jouppi et al., 2017), Eyeriss (Chen
et al., 2017), Thinker (Yin et al., 2017), etc.), for SNNs (e.g., SpiNNaker (Furber
et al., 2014), TrueNorth (Merolla et al., 2014), Loihi (Davies et al., 2018), DYNAPs
(Moradi et al., 2017)], and for cross-paradigm modeling [e.g., Tianjic (Pei et al.,
2019; Deng et al., 2020)]. In this Research Topic, we accepted seven papers for neural
network hardware: three for ANNs, two for SNNs, and two for cross-paradigm.
Sim and Lee propose SC-CNN, the bitstream-based convolutional neural network (CNN)
inspired by stochastic computing (SC) that uses bitstreams to represent numbers, to
improve machine learning hardware. Benefitting from the CNN substrate, SC-CNN can
achieve high accuracy; further benefitting from SC, SC-CNN is highly efficient, scalable,
and fault-tolerant. Different from the common digital machine learning accelerators,
Kaiser, Faria et al. present a clockless autonomous probabilistic circuit, wherein
synaptic weights are read out in the form of analog voltages, for fast and efficient
learning with no use of digital computing. They demonstrate a circuit built with existing
technology to emulate the Boltzmann machine learning algorithm. Muller et al. introduce
bias matching, a top-down neural network design approach, to match the inductive biases
required in a machine learning system to the hardware constraints of its implementation.
To alleviate the high cost training of SNNs using backpropagation, Lee et al. propose
a spike-train level direct feedback alignment (ST-DFA) algorithm. Compared to the
state-of-the-art backpropagation learning algorithm, they demonstrate excellent performance
vs. overhead tradeoffs on FPGA for speech and image classification applications. Dutta
et al. propose an all ferroelectric field-effect transistors (FeFET)-based SNN hardware
that allows low-power spike-based information processing and co-localized memory and
computing. They implement a surrogate gradient supervised learning algorithm on their
efficient SNN platform, which further accounts for the impacts of device variation
and limited bit precision of on-chip synaptic weights on the classification accuracy.
Parsa et al. build a hierarchical pseudo agent-based multi-objective Bayesian hyperparameter
optimization framework for both software and hardware. They can not only maximize
the performance of the network, but also minimize the energy and area overheads of
the corresponding neuromorphic hardware. They validate the proposed framework using
both ANN and SNN models, which involves both deep learning accelerators [e.g., PUMA
(Ankit et al., 2019)] and neuromorphic hardware [e.g., DANNA2 (Mitchell et al., 2018)
and mrDANNA (Chakma et al., 2017)]. Instead of implementing ANNs and SNNs separately,
integration of them has become a promising direction to achieve further breakthroughs
toward AGI via complementary advantages (Pei et al., 2019). Therefore, the efficient
hardware that can support individual modeling of ANNs and SNNs as well as their hybrid
modeling is very important. This has been achieved by the cross-paradigm Tianjic platform
(Deng et al., 2020), based on which Wang et al. further present an end-to-end mapping
framework for implementing various hybrid neural networks. By constructing hardware
configuration schemes for four typical signal conversions and establishing a global
timing adjustment mechanism among different heterogeneous modules, they implement
hybrid models with low execution latency and low power consumption.
Conclusion
Machine learning and neuromorphic computing are two modeling paradigms on the road
toward AGI. ANNs have achieved tremendous breakthroughs in many intelligent applications
benefitting from big data, high-performance processors, effective learning algorithms,
and easy-to-use programming tools; in contrast, SNNs are still in its infant stage
and there is a dire need for more neuromorphic benchmarks. Through cross-discipline
research, we expect to understand and bridge the gap between neuromorphic computing
and machine learning. This Research Topic is just a small step in this direction,
and we look forward to more innovations that can achieve brain-like intelligence.
Author Contributions
All authors listed have made a substantial, direct and intellectual contribution to
the work, and approved it for publication.
Conflict of Interest
The authors declare that the research was conducted in the absence of any commercial
or financial relationships that could be construed as a potential conflict of interest.