NxTF: An API and Compiler for Deep Spiking Neural Networks on Intel Loihi
Bodo Rueckauer, Connor Bybee, Ralf Goettsche, Yashwardhan Singh,, Joyesh Mishra, Andreas Wild

TL;DR
This paper introduces NxTF, a specialized API and compiler for mapping deep spiking neural networks onto Intel Loihi neuromorphic hardware, enabling efficient deployment, model compression, and improved accuracy for event-driven processing tasks.
Contribution
NxTF provides a Keras-based programming interface and compiler optimized for deep SNNs on Loihi, supporting model distribution, compression, and achieving high resource utilization and accuracy.
Findings
Achieves 80% resource utilization across 16 Loihi chips for a 28-layer MobileNet.
Reports lowest error rate of 8.52% on CIFAR-10 using MobileNet on neuromorphic hardware.
Demonstrates effective model compression and energy-efficient processing.
Abstract
Spiking Neural Networks (SNNs) are a promising paradigm for efficient event-driven processing of spatio-temporally sparse data streams. SNNs have inspired the design and can take advantage of the emerging class of neuromorphic processors like Intel Loihi. These novel hardware architectures expose a variety of constraints that affect firmware, compiler and algorithm development alike. To enable rapid and flexible development of SNN algorithms on Loihi, we developed NxTF: a programming interface derived from Keras and compiler optimized for mapping deep convolutional SNNs to the multi-core Intel Loihi architecture. We evaluate NxTF on DNNs trained directly on spikes as well as models converted from traditional DNNs, processing both sparse event-based and dense frame-based data sets. Further, we assess the effectiveness of the compiler to distribute models across a large number of cores…
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