Optimized Real-Time Biomimetic Neural Network on FPGA for Bio-hybridization

Neurological diseases can be studied by performing bio-hybrid experiments using a real-time biomimetic Spiking Neural Network (SNN) platform. The Hodgkin-Huxley model offers a set of equations including biophysical parameters which can serve as a base to represent different classes of neurons and affected cells. Also, connecting the artificial neurons to the biological cells would allow us to understand the effect of the SNN stimulation using different parameters on nerve cells. Thus, designing a real-time SNN could useful for the study of simulations of some part of the brain. Here, we present a different approach to optimize the Hodgkin-Huxley equations adapted for Field Programmable Gate Array (FPGA) implementation. The equations of the conductance have been unified to allow the use of same functions with different parameters for all ionic channels. The low resources and high-speed implementation also include features, such as synaptic noise using the Ornstein–Uhlenbeck process and different synapse receptors including AMPA, GABAa, GABAb, and NMDA receptors. The platform allows real-time modification of the neuron parameters and can output different cortical neuron families like Fast Spiking (FS), Regular Spiking (RS), Intrinsically Bursting (IB), and Low Threshold Spiking (LTS) neurons using a Digital to Analog Converter (DAC). Gaussian distribution of the synaptic noise highlights similarities with the biological noise. Also, cross-correlation between the implementation and the model shows strong correlations, and bifurcation analysis reproduces similar behavior compared to the original Hodgkin-Huxley model. The implementation of one core of calculation uses 3% of resources of the FPGA and computes in real-time 500 neurons with 25,000 synapses and synaptic noise which can be scaled up to 15,000 using all resources. This is the first step toward neuromorphic system which can be used for the simulation of bio-hybridization and for the study of neurological disorders or the advanced research on neuroprosthesis to regain lost function

Implementation of bioinspired algorithms on the neuromorphic VLSI system SpiNNaker 2

It is believed that neuromorphic hardware will accelerate neuroscience research and enable the next generation edge AI. On the other hand, brain-inspired algorithms are supposed to work efficiently on neuromorphic hardware. But both processes don't happen automatically. To efficiently bring together hardware and algorithm, optimizations are necessary based on the understanding of both sides. In this work, software frameworks and optimizations for efficient implementation of neural network-based algorithms on SpiNNaker 2 are proposed, resulting in optimized power consumption, memory footprint and computation time. In particular, first, a software framework including power management strategies is proposed to apply dynamic voltage and frequency scaling (DVFS) to the simulation of spiking neural networks, which is also the first-ever software framework running a neural network on SpiNNaker 2. The result shows the power consumption is reduced by 60.7% in the synfire chain benchmark. Second, numerical optimizations and data structure optimizations lead to an efficient implementation of reward-based synaptic sampling, which is one of the most complex plasticity algorithms ever implemented on neuromorphic hardware. The results show a reduction of computation time by a factor of 2 and energy consumption by 62%. Third, software optimizations are proposed which effectively exploit the efficiency of the multiply-accumulate array and the flexibility of the ARM core, which results in, when compared with Loihi, 3 times faster inference speed and 5 times lower energy consumption in a keyword spotting benchmark, and faster inference speed and lower energy consumption for adaptive control benchmark in high dimensional cases. The results of this work demonstrate the potential of SpiNNaker 2, explore its range of applications and also provide feedback for the design of the next generation neuromorphic hardware