Phase-of-firing code

Cattani, Anna; Einevoll, Gaute T.; Panzeri, Stefano

doi:10.48550/arxiv.1504.03954

Cited by 2 publications

(2 citation statements)

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“…An alternative approach uses temporal coding to represent data in the neural network. The traditional temporal coding schemes include time-to-first-spike (TTFS; Mostafa, 2016), where information is represented by the relative time of arrival of the spikes with respect to the first spike (see Figure 1B), and phaseof-firing, where information is encoded using the time at which neurons fire within a periodic cycle (Cattani et al, 2015). When information is encoded in the TTFS scheme, neurons in an SNN only spike once during each inference pass and see many fewer spikes than their rate-based counterparts.…”

Section: Spiking Neural Networkmentioning

confidence: 99%

A TTFS-based energy and utilization efficient neuromorphic CNN accelerator

Xiang

Srivatsa

et al. 2023

Front. Neurosci.

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Spiking neural networks (SNNs), which are a form of neuromorphic, brain-inspired AI, have the potential to be a power-efficient alternative to artificial neural networks (ANNs). Spikes that occur in SNN systems, also known as activations, tend to be extremely sparse, and low in number. This minimizes the number of data accesses typically needed for processing. In addition, SNN systems are typically designed to use addition operations which consume much less energy than the typical multiply and accumulate operations used in DNN systems. The vast majority of neuromorphic hardware designs support rate-based SNNs, where the information is encoded by spike rates. Generally, rate-based SNNs can be inefficient as a large number of spikes will be transmitted and processed during inference. One coding scheme that has the potential to improve efficiency is the time-to-first-spike (TTFS) coding, where the information isn't presented through the frequency of spikes, but instead through the relative spike arrival time. In TTFS-based SNNs, each neuron can only spike once during the entire inference process, and this results in high sparsity. The activation sparsity of TTFS-based SNNs is higher than rate-based SNNs, but TTFS-based SNNs have yet to achieve the same accuracy as rate-based SNNs. In this work, we propose two key improvements for TTFS-based SNN systems: (1) a novel optimization algorithm to improve the accuracy of TTFS-based SNNs and (2) a novel hardware accelerator for TTFS-based SNNs that uses a scalable and low-power design. Our work in TTFS coding and training improves the accuracy of TTFS-based SNNs to achieve state-of-the-art results on the MNIST and Fashion-MNIST datasets. Meanwhile, our work reduces the power consumption by at least 2.4×, 25.9×, and 38.4× over the state-of-the-art neuromorphic hardware on MNIST, Fashion-MNIST, and CIFAR10, respectively.

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Section: Spiking Neural Networkmentioning

confidence: 99%

A TTFS-based energy and utilization efficient neuromorphic CNN accelerator

Xiang

Srivatsa

et al. 2023

Front. Neurosci.

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“…The SMO can replace the external reference frame and thus overcome the precision issue. What's more, with the help of SMO, the phase of firing encoding scheme can be implemented in neuromorphic computing systems [32][33][34]. In the phase encoding, input signals are transferred to the phase of SMO.…”

Section: Examples Of Encoding Scheme (A) Presentation Of Rate Encodin...mentioning

confidence: 99%

Spiking Neural Encoding and Hardware Implementations for Neuromorphic Computing

Zheng,

(Cindy) Yi

2023

Neuromorphic Computing

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Due to the high requirements of the computational power of modern data-intensive applications, the traditional von Neumann structure and neuromorphic computing structure started to play complementary roles in the area of computing. Thus, neuromorphic computing architectures have attracted much attention with high data capacity and power efficiency. In this chapter, the basic concept of neuromorphic computing is discussed, including spiking codes and neurons. The spiking encoder can transfer analog signals to spike signals, thus avoiding using power-consuming analog-to-digital converters. Comparisons of training accuracy and robustness of neural codes are carried out, and the circuit implementations of the spiking temporal encoders are briefly introduced. The encoding schemes are evaluated on the PyTorch platform with the most common datasets, such as Modified National Institute of Standards and Technology (MNIST), Canadian Institute for Advanced Research, 10 classes (CIFAR-10), and The Street View House Numbers (SVHN). From the result, the multiplexing temporal code has shown high data capacity, robustness, and low training error. It achieves at least 6.4% more accuracy than other state-of-the-art works using other encoding schemes.

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Phase-of-firing code

Cited by 2 publications

References 0 publications

A TTFS-based energy and utilization efficient neuromorphic CNN accelerator

A TTFS-based energy and utilization efficient neuromorphic CNN accelerator

Spiking Neural Encoding and Hardware Implementations for Neuromorphic Computing

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