The project main goal is to develop a digital implementation in FPGA of a Spiking Restricted Boltzmann Machine (SRBM) and apply this architecture in real-life inference and classification tasks using imec-nl state-of-the-art bio-sensors.
In this project you will focus on the synthesis of spiking Restricted Boltzmann Machines (SRBM) which will be analyzed as Synaptic Sampling Machines and Generative Models . Inference and learning in RBMs and DBNs is achieved via Markov Chain Monte Carlo procedure called Gibbs sampling. It has already been shown that this procedure can be implemented using spiking neurons under certain circumstances .
This project will explore relations among neuromorphic Restricted Boltzmann Machines and current state-of-the-art deep learning mechanisms as DropConnect and Dropouts. During the course of the project, we will explore bio realistic learning rules as Spike Timing Dependent Plasticity (STDP) and event-driven Contrastive Divergence (eCD) as the main mechanism for weight update.
In collaboration with your colleagues, you will work on a number of challenges to the design of efficient brain-like chips: scalable, energy-efficient, highly flexible network architectures that can learn with incredible efficiency.
Click on ‘apply’ to submit your application. You will then be redirected to e-recruiting. If you know someone who could be the right fit for the job click on ’refer’.
Please be advised that non-EU/EEA country students that are studying outside of the Netherlands, need to have a work-permit to be able to do an internship in the Netherlands.