  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.
