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Neuromorphic Intelligence In An Embedded System in Collaboration with AiCTX

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Short Description

aiCTX is a leading-edge neuromorphic computing company. It provides dedicated mixed-signal neuromorphic processors which overcome the limitations of legacy von Neumann computers to provide an unprecedented combination of ultra-low power consumption and low-latency performance. aiCTX has a unique technological edge and IP portfolio that comes from over 20 years of experience in mixed-signal neural processor design, advanced neural routing architectures, and neural algorithms. Starting from 2019 IIS and aiCTX are providing several semester and master projects in the field of neuromorphic intelligence using their processor to build a whole working embedded system. The student will deal with both hardware and software building a prototype to cover a specifc application. In particular we identify 3 possibile projects but more can be found:

More Algorithms project topic

Reservoir computing

   Benchmark how varying percentage of mismatch, for difference neuron / synapse parameters, degrades reservoir performance, for a fixed network between chips.
   Training output layer for reservoir networks, in the presence of mismatch between chips. Guarantee that the resulting network has equivalent / baseline performanc between chips
   Various forms of structured reservoirs, applied to specific tasks
       Wilson/Cowan oscillator reservoirs
       Excitatory subnetwork reservoirs
       Correlation-based recurrence reservoirs
   Implement one in-reservoir training approach, using our pipeline
       FORCE
       full-FORCE
       Devene / Machens / etc
   Implement one reservoir transfer approach, using our pipeline
       non-spiking → spiking
       spiking → DynapSE in loop
       non-spiking → DynapSE in loop

Vision processing

   Object tracking with SCNNs and benchmarking
   Dataset generation for surveillance with DAVIS
       Data collection and labeling
       Model development
   Implementation of standard CNN models with SNNs
       (Like Lenet 5 example in our documentation)
       Inception
       Resnet
   Investigate implementing recurrent CNN networks using sinabs SW package

Hardware-software projects suggestions

   Mutual-information based optimisation of signal processing front end
       Apply to wake-phrase or audio application
       Or vibration-based fault detection
   True anomaly detection with on-line learning
       Vibration-based anomaly detection
       Applying algorithms from Robert Legenstein’s group
   Spoken phoneme recognition network
   Voice-activity detector use case demo
   Explore weight-agnostic networks for mixed-signal reservoirs 


Depending on the applicant's profile and project type, his tasks may involve some of the following:

Prerequisites

(not all need to be met by the single candidate)

  • Experience using the laboratory instrumentation - signal generators, oscilloscopes, DAQ cards, Matlab etc..
  • knowledge of microcontroller programming and PC programming (C/C++, preferably embedded C)
  • basic knowledge or interests on power converters, wireless communication, and circuit design at a components level (IC design is NOT involved)
  • Motivation to build and test a real system
  • PCB Desing or willing to learn it
  • Machine learning and deep learning on PC and microcontroller (or the motivation/interess to learn it)
  • Basic or strong motivation to learn Neuromorphic Ingtelligence

Figure's source: https://aictx.ai/ Detailed Task Description A detailed task description will be worked out right before the project, taking the student's interests and capabilities into account.


Status: Available

  • Looking for Semester and Master Project Students
Supervisors: Michele Magno


Character

35% Theory
45% Implementation
20% Testing

IIS Professor

Luca Benini

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Detailed Task Description

Goals

Practical Details

Results