Fast and energy-efficient neuromorphic deep learning with first-spike times (1912.11443v4)

Abstract: For a biological agent operating under environmental pressure, energy consumption and reaction times are of critical importance. Similarly, engineered systems are optimized for short time-to-solution and low energy-to-solution characteristics. At the level of neuronal implementation, this implies achieving the desired results with as few and as early spikes as possible. With time-to-first-spike coding both of these goals are inherently emerging features of learning. Here, we describe a rigorous derivation of a learning rule for such first-spike times in networks of leaky integrate-and-fire neurons, relying solely on input and output spike times, and show how this mechanism can implement error backpropagation in hierarchical spiking networks. Furthermore, we emulate our framework on the BrainScaleS-2 neuromorphic system and demonstrate its capability of harnessing the system's speed and energy characteristics. Finally, we examine how our approach generalizes to other neuromorphic platforms by studying how its performance is affected by typical distortive effects induced by neuromorphic substrates.

• The paper presents a method for fast, energy-efficient deep learning in spiking neural networks (SNNs) on neuromorphic hardware using time-to-first-spike (TTFS) coding.
• It derives learning rules compatible with backpropagation that enable exact gradient calculation based on spike times in leaky integrate-and-fire (LIF) neurons.
• The approach is demonstrated on the BrainScaleS-2 platform, showing effective and robust classification performance on datasets like MNIST and Yin-Yang, highlighting potential for energy efficiency.

Fast and Energy-Efficient Neuromorphic Deep Learning with First-Spike Times

This paper investigates the intersection of biological principles and engineered computing systems, aiming to create energy-efficient and rapid computation by leveraging neuromorphic hardware optimized for spike-based neural networks. The focus is on spiking neural networks (SNNs) and deploying their intrinsic benefits to design architectures that could challenge the efficiency and computational prowess of the human brain.

Neuromorphic Computation and Time-to-First-Spike Coding

Neuromorphic systems, inspired by the structure and function of biological neural networks, have the potential to provide energy-efficient computing options. The paper specifically explores the use of the BrainScaleS-2 neuromorphic system, demonstrating how backpropagation algorithms can be implemented for learning in hierarchical SNNs. The novel approach relies solely on input and output spike times and leverages "time-to-first-spike" (TTFS) coding. This coding scheme represents information with the time elapsed before a neuron's first spike, promoting computations that are both sparse and rapid, akin to physiological attributes in biological organisms.

Theoretical Framework and Derivation of Learning Rules

Fundamental to the authors' approach is the derivation of learning rules suitable for networks of leaky integrate-and-fire (LIF) neurons. Traditional learning methods like backpropagation have typically been incompatible with SNNs; however, through meticulous mathematical formulation, the authors successfully bridge this gap.

For specific configurations of neuronal time constants, the authors present analytical solutions, allowing exact calculation of the gradients of cost functions dependent on spike times. Exact error propagation ensures rigorous training, making SNNs capable of universal classification across varied data spaces, whether discrete label sets or continuous stimuli.

Emulations on the BrainScaleS-2 Neuromorphic Platform

The authors show how their framework can be applied to the BrainScaleS-2 platform, showcasing energy-efficient processing and rapid classification on benchmark tasks such as the Yin-Yang and MNIST datasets. The paper confirms the robustness of their algorithms as they function reliably even when faced with substrate-induced imperfections like manufacturing variability and parameter jitter.

Implications and Future Directions

Deploying neuromorphic hardware for AI tasks and leveraging TTFS coding holds promise for developing systems with capabilities similar to those of biological brains, especially in terms of energy efficiency and speed. However, despite achieving promising results, there remains a broader challenge in scaling such algorithms and hardware for more complex tasks in AI. Additionally, insights from this work may inform future strategies for implementing learning mechanisms in biological systems. The flexibility of exact spike-time manipulation allows the model to branch into more complex coding schemes and network structures beyond TTFS, such as multi-spike coding and recurrent models for processing dynamic data streams.

Overall, though implementation challenges remain, this paper successfully marks a step towards integrating biological realism into computational systems, underscoring the importance of neuromorphic hardware as a viable alternative to conventional machine learning approaches. The work encourages future explorations into specialized neuromorphic solutions, particularly for edge computing environments.