Neuromorphic models take inspiration from the human brain by adopting bio-plausible neuron models to build alternatives to traditional Machine Learning (ML) and Deep Learning (DL) solutions. The scarce availability of dedicated hardware able to actualize the emulation of brain-inspired computation, which is otherwise only simulated, yet still hinders the wide adoption of neuromorphic computing for edge devices and embedded systems. With this premise, we adopt the perspective of neuromorphic computing for conventional hardware and we present the L2MU, a natively neuromorphic Legendre Memory Unit (LMU) which entirely relies on Leaky Integrate-and-Fire (LIF) neurons. Specifically, the original recurrent architecture of LMU has been redesigned by modelling every constituent element with neural populations made of LIF or Current-Based (CuBa) LIF neurons. To couple neuromorphic computing and off-the-shelf edge devices, we equipped the L2MU with an input module for the conversion of real values into spikes, which makes it an encoding-free implementation of a Recurrent Spiking Neural Network (RSNN) able to directly work with raw sensor signals on non-dedicated hardware. As a use case to validate our network, we selected the task of Human Activity Recognition (HAR). We benchmarked our L2MU on smartwatch signals from hand-oriented activities, deploying it on three different commercial edge devices in compressed versions too. The reported results remark the possibility of considering neuromorphic models not only in an exclusive relationship with dedicated hardware but also as a suitable choice to work with common sensors and devices.

翻译：神经形态模型借鉴人脑机制，采用生物可解释的神经元模型构建传统机器学习（ML）与深度学习（DL）方案的替代范式。然而，能够实现类脑计算仿真的专用硬件稀缺（目前主要依赖模拟运行），这仍制约着神经形态计算在边缘设备与嵌入式系统中的广泛应用。基于此背景，我们立足传统硬件的神经形态计算视角，提出L2MU——一种完全基于泄漏积分发放（LIF）神经元的原生神经形态勒让德记忆单元（LMU）。具体而言，我们通过使用LIF或电流型（CuBa）LIF神经元构成的神经群体对LMU原始循环架构的每个组成单元进行重构。为实现神经形态计算与商用边缘设备的结合，我们为L2MU配备了将实值转换为脉冲的输入模块，使其成为可直接在非专用硬件上处理原始传感器信号的无编码循环脉冲神经网络（RSNN）实现。为验证网络性能，我们选取人体活动识别（HAR）任务作为应用场景。基于手部导向活动的智能手表信号，我们在三种不同商用边缘设备上对压缩版L2MU进行部署与性能评估。实验结果表明，神经形态模型不仅可与专用硬件协同工作，更能成为处理常见传感器与设备的有效解决方案。