Neural Architecture Achieves 50% Lower Latency With Elastic Dataflow And On-the-Fly Quantum KFormer Execution

Spiking neural networks offer a pathway to more energy-efficient computing than traditional artificial neural networks, but realising this potential requires overcoming challenges related to processing sparse data and managing execution delays. Yuehai Chen and Farhad Merchant, from the Bernoulli Institute and CogniGron at the University of Groningen, address these issues with NEURAL, a novel computer architecture that combines data-driven and event-driven processing. This innovative system uses a flexible ‘first-in-first-out’ approach and integrates a new method for converting data into spike patterns, enabling full-spike execution without needing specialised hardware. The team demonstrates that NEURAL significantly improves both accuracy and energy efficiency, achieving a 50% reduction in resource use and nearly doubling performance compared to existing spiking neural network accelerators, and boosting accuracy on image recognition tasks by over 3% and 5% on standard datasets.

Neuromorphic Computing With Spiking QKFormer Networks

This research introduces NEURAL, a novel neuromorphic architecture that efficiently executes spiking neural networks (SNNs) while minimizing hardware requirements. The team achieved this through a hybrid data-event execution paradigm, allowing for flexible communication within the architecture. A key innovation is the Spiking QKFormer, a spiking version of the powerful QKFormer transformer architecture, adapted for SNNs. To train these networks, scientists developed a knowledge distillation (KD) framework, enabling high accuracy in single time-step execution. Furthermore, the team replaced traditional activation layers with a Weight-to-Time-to-Firing-Spike (W2TTFS) mechanism, streamlining computation for full-spike execution.

The researchers optimized models using operator fusion, quantization, and KD-based quantization-aware training, preparing them for deployment on dedicated hardware. Experimental results demonstrate that NEURAL achieves high accuracy on benchmark datasets, such as CIFAR-10, while significantly reducing hardware resource consumption compared to existing SNN accelerators. The architecture exhibits high computational efficiency and normalized efficiency, outperforming systems like SiBrain, Cerebron, and STI-SNN in terms of accuracy, performance, and efficiency.

Spiking Network Training via Knowledge Distillation

Scientists engineered NEURAL, a novel neuromorphic computing architecture that overcomes limitations in existing spiking neural network (SNN) hardware, specifically addressing latency and energy efficiency issues caused by spike sparsity and multi-timestep execution. To achieve single-timestep inference, they developed a knowledge distillation (KD)-based training framework, enabling SNNs to attain competitive accuracy without requiring multiple processing steps. This framework combines KD with fixed-point quantization, creating single-timestep SNNs that rival the performance of multi-timestep models. A key innovation is the window-to-time-to-first-spike (W2TTFS) mechanism, which transforms traditional average pooling, a non-spiking operation, into a spike-based computation, maintaining accuracy while enhancing energy efficiency.

The team implemented a hybrid data-event execution paradigm, utilizing elastic first-in-first-out (FIFO) scheduling to enable data-driven control and event-driven neuron computation. This supports on-the-fly spiking QKFormer without requiring dedicated hardware units, streamlining the computational process. Validating the architecture on a Xilinx Virtex-7 FPGA, the team deployed deep SNNs, achieving a 3. 20% accuracy improvement on the CIFAR-10 dataset and a 5. 13% improvement on CIFAR-100. At the architectural level, NEURAL achieves a 50% reduction in resource utilization and a 1. 97x improvement in energy efficiency compared to existing SNN accelerators.

Single-Timestep Spiking Neural Networks via Knowledge Distillation

Scientists have developed NEURAL, a novel computing architecture for spiking neural networks (SNNs) that significantly improves energy efficiency and reduces latency. The team achieved single-timestep inference, eliminating the need for complex scheduling logic and reducing computational demands. A key innovation is a window-to-time-to-first-spike (W2TTFS) mechanism, which successfully converts non-spiking average pooling operations into spike-based computation without compromising accuracy. The research team also introduced a knowledge distillation (KD)-based training framework, enabling the creation of single-timestep SNN models with competitive accuracy.

Experiments demonstrate that, using this framework, models achieved accuracies of up to 93. 46% on CIFAR-10 and 72. 1% on CIFAR-100. Furthermore, deploying NEURAL on a Xilinx Virtex-7 FPGA, the team achieved a 50% reduction in resource utilization compared to existing SNN accelerators. Measurements confirm a 1. 97x improvement in energy efficiency, demonstrating the potential of this hybrid data-event execution paradigm.

Efficient Spiking Neural Networks with NEURAL

This research presents NEURAL, a novel neuromorphic architecture designed to improve the efficiency of spiking neural networks. By combining data-driven and event-driven processing, and employing elastic interconnection, the team achieved significant reductions in resource utilization and improvements in energy efficiency when compared to existing SNN accelerators. Experimental results demonstrate a 50% reduction in resource consumption and a 1. 97x increase in energy efficiency. The team also introduced a knowledge distillation-based training framework that enables single-timestep SNN models to achieve competitive accuracy, and a window-to-time-to-first-spike mechanism that replaces traditional average pooling layers, facilitating full-spike execution. Evaluations demonstrate accuracy improvements on benchmark datasets, alongside substantial gains in computational efficiency.