… the power and efficiency of spiking neural networks. These networks were able to achieve …
did so in far fewer operations and with a shorter latency. Due to the efficiency of a spike-based …

… To effectively train SNNs with low latency, we further study the representation error …
neural models to reduce the error. With these methods, we can train high-performance low-latency …

… We design LEARN code for low latency requirements with specific network designs. …
neural network architecture for LEARN (both encoder and decoder) that satisfies the low latency …

… neural network (DNN). In this paper, we determine that SOTA conversion strategies cannot
yield ultra low latency … The resulting SNNs have ultra low latency and high activation sparsity, …

… However, there is a trade-off between accuracy and latency. … a spiking neuron with the
activation value of an analog neuron, … converted SNNs with extremely low latency (fewer than 32 …

… reduction in model size, latency and energy consumption. … versions of deep neural network
models for minimum-energy… resources are strictly limited and a latency of\({\mathcal {O}}(1…

… trade-off between low latency and high … -low latency (within 4 time-steps). Second, we
consider the best accuracy of SNN. It is obvious to find that the SNN accuracy at ultra-low latency …

… An alternative route for efficient computation is using Spiking Neural Networks (SNNs) [9,
10]… networks. In this paper, standard networks are referred to as Analog Neural Networks (ANNs…

… To our best knowledge, the only high-level tool that can achieve comparable latency for
neural network inference in FPGA is hls4ml [10]. This tool was designed to work in particle …

… Section 2 describes the low latency benchmark task in this work related to jet classification
at the CERN Large Hadron Collider (LHC). Section 3 introduces our approach to QAP and the …