Ouwen Jin

Many-core neuromorphic systems serve as specialized accelerators for Spiking Neural Networks (SNNs). However, the communication mechanisms in existing neuromorphic systems incur significant traffic and energy overhead due to redundant address transmissions, a bottleneck exacerbated by the short payloads inherent to the spike-based communication nature of SNNs. Specifically, our characterization reveals that in current neuromorphic systems, duplicate address transmissions can account for up to 49% of the total traffic in representative workloads. This paper presents UniSpike, a hardware-software co-design that eliminates address redundancy by aggregating spikes destined for the same core into single, compact packets. UniSpike implements a spike transmission scheduling strategy to enable efficient spike merging, supported by a dedicated hardware architecture for runtime packet assembly and dispatch, as well as a destination-aware SNN partitioning algorithm that maximizes address sharing opportunities. Experimental results demonstrate that on average, UniSpike reduces traffic volume by 1.93×, achieving 1.77× speedup and 1.50× energy efficiency improvement over SOTA designs.

Neuromorphic systems, designed to execute Spiking Neural Networks (SNNs) efficiently, feature a many-core architecture equipped with a Network-on-Chip (NoC) to facilitate spike communication. The distributed memory and decentralized computation require partitioning SNNs into multiple subnetworks, each executed on a neuromorphic core in parallel. However, the increasing scale of SNNs leads to high inter-core spike traffic and expensive computational cost of partitioning algorithms. To address these challenges, we propose SNNcut, an efficient partitioning method for large-scale SNNs using spike-sharing. This approach fully exploits spike-sharing by using one NoC packet to deliver multiple spikes of information to reduce spike traffic. Meanwhile, instead of exhaustively searching over neurons in SNN, SNNcut indexes neurons based on the layer connection pattern and clusters sequentially, which significantly reduces the computational complexity and ensures the efficiency of large-scale SNN partitioning. Experimental results demonstrate that SNNcut achieves an average reduction in spike traffic of 98.97%, compared to 74.98% from state-of-the-art existing methods. In addition, the SNNs partitioned by SNNcut require only 5.7%∼15.7% neuromorphic core consumption of previous works. In a large-scale scenario, existing methods take more than 100 hours to accomplish the partitioning, while SNNcut only needs 10 minutes. We also validate the scalability of SNNcut by testing on edge cases with 3.2B neurons and 902B synapses.

Spiking Neural Networks (SNNs) are widely used in brain-inspired computing and neuroscience research. Several many-core accelerators have been built to improve the running speed and energy efficiency of SNNs. However, current accelerators generally need explicit synchronization among all cores after each timestep of SNNs, which poses a challenge to overall efficiency. This paper proposes DepAsync, an asynchronous architecture that eliminates inter-core synchronization, facilitating fast and energy-efficient SNN inference with commendable scalability. The main idea is to exploit the dependency of neuromorphic cores predetermined at compile time. We design a DepAsync scheduler for each core to trace the running state of its dependencies and control the core to safely forward to the next timestep without waiting for other cores to complete their tasks. This approach prevents the necessity for global synchronization, allowing DepAsync to minimize core waiting time facing inherent core and time imbalance in SNN workloads. The comprehensive evaluations using five SNN workloads show that DepAsync achieves 2.47x speedup and 1.55x energy efficiency compared to the state-of-the-art synchronization architectures.

Neuromorphic hardware systems—designed as 2D-mesh structures with parallel neurosynaptic cores—have proven highly efficient at executing large-scale spiking neural networks (SNNs). A critical challenge, however, lies in mapping neurons efficiently to these cores. While existing approaches work well with regular, fully functional mesh structures, they falter in real-world scenarios where hardware has irregular shapes or non-functional cores caused by defects or resource fragmentation. To address these limitations, we propose a novel mapping method based on an innovative space-filling curve: the Adaptive Locality-Preserving (ALP) curve. Using a unique divide-and-conquer construction algorithm, the ALP curve ensures adaptability to meshes of any shape while maintaining crucial locality properties—essential for efficient mapping. Our method demonstrates exceptional computational efficiency, making it ideal for large-scale deployments. These distinctive characteristics enable our approach to handle complex scenarios that challenge conventional methods. Experimental results show that our method matches state-of-the-art solutions in regular-shape mapping while achieving significant improvements in irregular scenarios, reducing communication overhead by up to 57.1%.