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）的替代方案。然而，能够实现类脑计算仿真的专用硬件稀缺（目前仅能通过模拟实现），这仍然阻碍着神经形态计算在边缘设备与嵌入式系统中的广泛应用。基于此前提，我们采用面向传统硬件的神经形态计算视角，提出L2MU——一种完全依赖泄漏积分发放（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元素进行建模，从而重新设计了该架构。为将神经形态计算与商用边缘设备结合，我们为L2MU配备了将实数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循环脉冲神经网络（RSNN）免编码实现方案。为验证网络性能，我们选择人体活动识别（HAR）任务作为应用场景。基于手部活动智能手表信号对L2MU进行基准测试，并将其压缩版本部署于三种不同商业边缘设备。实验结果表明，神经形态模型不仅可与专用硬件结合，也能成为处理常规传感器与设备的有效选择方案。