To Spike or Not To Spike: A Digital Hardware Perspective on Deep Learning Acceleration (2306.15749v5)

Abstract: As deep learning models scale, they become increasingly competitive from domains spanning from computer vision to natural language processing; however, this happens at the expense of efficiency since they require increasingly more memory and computing power. The power efficiency of the biological brain outperforms any large-scale deep learning ( DL ) model; thus, neuromorphic computing tries to mimic the brain operations, such as spike-based information processing, to improve the efficiency of DL models. Despite the benefits of the brain, such as efficient information transmission, dense neuronal interconnects, and the co-location of computation and memory, the available biological substrate has severely constrained the evolution of biological brains. Electronic hardware does not have the same constraints; therefore, while modeling spiking neural networks ( SNNs) might uncover one piece of the puzzle, the design of efficient hardware backends for SNN s needs further investigation, potentially taking inspiration from the available work done on the artificial neural networks ( ANNs) side. As such, when is it wise to look at the brain while designing new hardware, and when should it be ignored? To answer this question, we quantitatively compare the digital hardware acceleration techniques and platforms of ANNs and SNN s. As a result, we provide the following insights: (i) ANNs currently process static data more efficiently, (ii) applications targeting data produced by neuromorphic sensors, such as event-based cameras and silicon cochleas, need more investigation since the behavior of these sensors might naturally fit the SNN paradigm, and (iii) hybrid approaches combining SNN s and ANNs might lead to the best solutions and should be investigated further at the hardware level, accounting for both efficiency and loss optimization.

A Digital Hardware Perspective on Deep Learning Acceleration: ANNs versus SNNs

The paper "To Spike or Not To Spike: A Digital Hardware Perspective on Deep Learning Acceleration" critically evaluates the landscape of deep learning (DL) acceleration from a hardware perspective, focusing on artificial neural networks (ANNs) and spiking neural networks (SNNs). It thoroughly investigates digital hardware implementations, comparing the efficiency of these neural models through the lens of real-world applications and benchmarks, particularly emphasizing object recognition and temporal tasks like keyword spotting (KWS) and voice activity detection (VAD).

At its core, the paper discusses the distinct computational architectures of ANNs and SNNs. ANNs perform computations in a static, deterministic manner, making them well-suited to static data processing. In contrast, SNNs simulate the temporal dynamics of neuronal spike firing, offering potential efficiency benefits for tasks with temporal components.

Numerical Insights and Efficiency Comparison

A key contribution of the paper is its detailed energy consumption model for convolution operations in both ANN and SNN architectures. By quantifying data access (memory reads and writes), arithmetic operations, and state updates, the analysis provides numerical estimates of energy requirements for typical tasks.

1. Static Tasks: For static computer vision tasks, such as object recognition on ImageNet, ANN accelerators generally outperform SNNs in terms of efficiency and accuracy. This disparity arises because SNNs need multiple timesteps to process static inputs, negating any efficiency gained from sparse spike computations. The analysis shows ANN models complete inference in fewer operations, leading to reduced energy consumption.
2. Temporal Tasks: In temporal tasks like audio processing, SNN accelerators demonstrate competitive efficiency. The paper compares various implementations and highlights that the energy consumption per inference generally favors SNNs given the temporal nature of spikes aligning naturally with sequential data. One notable case is Frenkel’s full on-chip learning accelerator, which is a significant landmark in providing efficient, adaptive intelligence with SNNs.

Implications and Future Directions

The implications of this work emphasize that while SNNs are not the optimal choice for static data, they show promise in handling temporal workloads efficiently. The research suggests further exploration into mixed solutions involving hybrid ANN-SNN architectures, aiming for a balance that leverages the strengths of both paradigms.

Future developments might include leveraging bio-inspired sensors and event-based data, natural fits for SNN models. However, the paper calls for further enhancement in SNN model training techniques to match the classification accuracy of ANNs better, reinforcing an area ripe for advancement.

Conclusion

This paper meticulously addresses the pivotal question of when to use SNNs for deep learning tasks. It delineates scenarios where SNNs can provide energy benefits, particularly with a promising outlook on learning adaptability and temporal data processing. By offering insights into both models and raising awareness of hybrid solutions, the paper contributes to understanding efficient digital hardware design and prompts innovation in neuromorphic computing.