用于在商用边缘设备上实现免编码的基于SNN的HAR的原生神经形态LMU架构
Natively neuromorphic LMU architecture for encoding-free SNN-based HAR on commercial edge devices
AI总结：
提出原生神经形态的L2MU架构，基于LIF神经元重新设计LMU并实现免编码RSNN，在商用边缘设备上直接处理原始传感器信号进行HAR，证明神经形态模型在非专用硬件上的可行性。
摘要：
神经形态模型通过采用生物逼真的神经元模型来构建传统机器学习（ML）和深度学习（DL）解决方案的替代方案，从而从人脑中汲取灵感。然而，能够实际化模拟脑启发计算的专用硬件的稀缺可用性（否则只能进行模拟），仍然阻碍了神经形态计算在边缘设备和嵌入式系统中的广泛采用。基于这一前提，我们采用传统硬件的神经形态计算视角，提出了L2MU，这是一种原生神经形态的Legendre Memory Unit（LMU），它完全依赖于Leaky Integrate-and-Fire（LIF）神经元。具体来说，LMU的原始循环架构已被重新设计，通过用由LIF或Current-Based（CuBa）LIF神经元组成的神经种群来建模每个组成元素。为了将神经形态计算与现成的边缘设备相结合，我们为L2MU配备了一个输入模块，用于将实值转换为脉冲，这使其成为Recurrent Spiking Neural Network（RSNN）的免编码实现，能够直接在非专用硬件上处理原始传感器信号。作为验证我们网络的用例，我们选择了Human Activity Recognition（HAR）任务。我们在来自手部导向活动的智能手表信号上对我们的L2MU进行了基准测试，并将其以压缩版本部署在三种不同的商用边缘设备上。报告的结果强调了将神经形态模型不仅视为与专用硬件的独占关系，而且视为与常见传感器和设备一起工作的合适选择的可能性。
英文摘要：
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.
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