To understand and mimic the working mechanism of the brain, neuroscientists rely on brain simulations that operate in a time-driven manner. The simulation involves evaluating how the neurons change their states over time and transferring spikes to the connected neurons through synapses. It also simulates learning by evaluating how the synapses change their weights according to the spiking activity of the neurons. To explore various behaviors of the brain and thus make great advances, neuroscientists need a methodology to support large-scale simulations in both an accurate and efficient manner. For accurate simulations, existing simulators adopt a time-precise simulation methodology where the simulator computes all the neuronal and the synaptic state changes in time order. Unfortunately, they suffer from significant underutilization and energy inefficiency as the simulator scales.In this paper, we present NeuroSync, a fast, energy-efficient, and scalable hardware-based accelerator for accurate brain simulations. The key idea is to adopt a speculative simulation methodology at a minimum overhead along with architectural support. NeuroSync achieves high efficiency using an optimal dataflow for the speculative simulations. At the same time, it ensures simulation accuracy by carefully designing a rollback and recovery mechanism to handle mis-speculations. To implement the methodology at a low cost, NeuroSync further proposes a speculation-optimal learning simulation method. Our evaluations show that 64-chip NeuroSync achieves 3.37× speedup and 3.81× higher energy efficiency with only 10.96% area overhead. The evaluations also show that NeuroSync is extremely scalable with higher speedup as the system scales.

Brain-inspired computing aims to understand the cognitive mechanisms of a brain and apply them to advance various areas in computer science. Deep learning is an example to greatly improve the field of pattern recognition and classification by utilizing an artificial neural network (ANN). To exploit advanced mechanisms of a brain and thus make more great advances, researchers need a methodology that can simulate neural networks with higher computational capabilities such as advanced spiking neural networks (SNNs) with two-stage neurons and synaptic delays. However, existing SNN simulation methodologies are too slow and energy-inefficient due to their software-based simulation or hardware-based but time-driven execution mechanisms.