Restricted Boltzmann Machines (RBMs) represent fundamental elements in Deep Belief Networks (DBNs).
What you will do
Restricted Boltzmann Machines (RBMs) represent fundamental elements in Deep Belief Networks (DBNs). These types of architectures set the current state-of-the-art performance in a variety of task including speech analysis, image classification and motor control. The implementations of RBMs and DBNs in neuromorphic hardware can result beneficial for a variety of reasons which include:
The focus of this project is the synthesis of Spiking Restricted Boltzmann Machines (SRBM) in FPGA fabric. During the project we will explore bio-inspired learning algorithms as Spike Timing Dependent Plasticity (STDP) and event-driven Contrastive Divergence (eCD) in both supervised and unsupervised learning scenarios. 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 sensors.
As a student in the Neuromorphic team you get the chance to contribute in several challenges to the design of efficient brain-like chips: scalable, energy-efficient, highly flexible network architectures that can learn with incredible efficiency. The student will benefit by closely interacting with research scientists and engineers at imec-nl.
Click on ‘apply’ to submit your application. You will then be redirected to e-recruiting.
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 at imec the Netherlands.
Please note that to be considered for an internship you need to be registered as a student during the entire internship period. Formal documentation of which may be requested at any time.