CROSSCUT: A Multi-Core Neuromorphic Accelerator Improving Resource-Utilization

• Published in: IEEE International Symposium on Circuits and Systems proceedings pp. 1 - 5
• Main Authors: Yang, Youming, Zhong, Yi, Wang, Zilin, Zhang, Tao, Lun, Li, Cui, Yingying, Cui, Xiaoxin, Jia, Song, Wang, Yuan
• Format: Conference Proceeding
• Language: English
• Published: IEEE 25.05.2025
• Subjects: Adaptation models; artificial neural network; Computational modeling; Energy efficiency; Field programmable gate arrays; FPGA; Hardware; Integrated circuit modeling; neuromorphic computing; Neuromorphic engineering; Neurons; spiking neural network; Spiking neural networks; Synapses; utilization
• ISSN: 2158-1525
• DOI: 10.1109/ISCAS56072.2025.11043243

Neuromorphic computing is attracting significant attention due to its bio-mimetic characteristics. Consequently, neuromorphic hardware platforms have emerged as innovative computing architectures for acceleration. However, the fixed nature of data flow and resources leads to considerable inefficiencies in storage and computation, thereby limiting both utilization efficiency and overall performance. This severely hinders the deployment of edge artificial intelligence (AI) models. To address these issues, we present a multi-core neuromorphic accelerator named CROSSCUT. This crossbar-based system supports both spiking neural network (SNN) and artificial neural network (ANN) paradigms and has a capacity of 256K neurons and 288M synapses. By leveraging the Neuron Package Mechanism (NPM) and Synapse Compress Mechanism (SCM), CROSSCUT can increase input data scale by 64 times and reduce wasted resources and computations by 46.7%, ensuring high compatibility with diverse network structures in machine learning models. Additionally, a Tree-Mesh hybrid network on chip (NoC) is constructed for inter-core communication. Implemented on Xilinx XCVU9P FPGA, CROSSCUT can achieve a peak performance of 431.9 GSOPS/s and 121.13 GSOPS/W energy efficiency. The inference accuracy on MNIST is 98.2%.