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Meeting #4

Friday, 3/2/2022

Table of Contents

  1. Agenda
  2. Deliverables for Next Week
  3. Notes
    1. Reinforcement Learning
    2. Neuromorphic Computing


  1. Presentations, 2 groups + discussion ~20 min each
  2. Research/project ideation work time
  3. Develop a possible project list based on presentations

Meeting Recording for 1st Half, Meeting Recording for 2nd Half

Deliverables for Next Week

Groups that have not presented this week will be giving presentations next week.


  • Motivation - algorithms for computing don’t learn like humans do, how can we make them more like humans?
  • Take ideas and inspiration from neuroscience and manifest them in computing

Reinforcement Learning

  • Reinforcement - when you do something over and over again.
  • Learning - when information goes into your brain.
  • Combining reinforcement and learning - put information into your brain, make sure it stays there, and repeat.
  • Oxford Dictionary - defines intelligence as the ability to acquire and apply knowledge and skills
  • Reinforcement Learning - acquires knowledge as machines find the best possible behavior, done by learning from mistakes.
  • Ties to psychology - operant conditioning. Positive vs negative reinforcement.
    • Positive reinforcement gives a stimuli (either a reward or punishment) after an event
    • Negative reinforcement just doesn’t respond after an event / takes away the stimuli
  • Reinforcement learning in the field of Computer Science.
  • Reward hypothesis - any goal can be formalized as the outcome of maximizing a cumulative reward.
  • Formulate optimization problems as Markov Decision Processes - nodes represent states; an agent can take an action with some probability of doing that to get to a different node / state
  • Recent advancements in Reinforcement Learning.
    • Applications: self-driving cars, games (tetris, snake, etc.)
  • Deep Q Learning to estimate values of possible actions given the state

Access slides here

Neuromorphic Computing

See Chris Kang’s video on Neuromorphic Computing.

  • Goal - quick survey of the field and research. Opportunities for neuromorphic computing algorihtms and applications.
  • Brains have desirable properties - energy efficient, fast at learning, use unique computational operations
  • Hardware and software need to be co-designed: they can’t exist in isolation
    • Can be pretty foreign for most computer scientists.
    • If we change the underlying hardware, we must change the paradigm
    • Optimizing algorithms on phyiscal manisfestations
  • Applications - why do we care?
    • Edge computing (energetically efficient)
    • Machine learning (rapidly training and flexibility)
    • Coprocessor in heterogeneous systems
  • Edge computing - Loihi graph
  • ANNs to GPU are mathematical and abstractive approaches - we can try to run native simulations in hopes of being more efficient.
  • Set up the neural network on-device in which the neurons are built into the hardware on the chip.
  • Physical systems through hardware can be directly executed
  • If we do work with these, will be using simulations of neuromorphic chips.
  • Coprocessor for novel domains - how can neuromorphic computing be used for differential equation solving, graph problems, optimization, etc.
  • We can continue the spirit of Moore’s law using heterogeneous systems
  • Spiking Neural Network - generalizes to very broad neural networks, all nerual networks.
    • Use spike-based inputs instead of typical one-hot vectors or scalar values.
    • Spikes are temporal
    • Focus on being temporal and event-driven
    • Signals that propagate take time to reach their destinations
    • The focus is being on being able to obtain a threshold and sending a signal once it is reached
  • Theoretical guarantees of Spiking Neural Networks - SNNs are a superset of ANN functionality.
  • Hardware neurmophic architectures: silicon-based, exotic materials (eggs, etc.)
  • Co-design algorithms to the hardware
  • SNNs are generalized ANNs. ML: quasi-backpropagation. Turn existing ANns into SNNs. Resevoir appraoch - take a soup of neurons and have a normal-ML interpret at the end.
  • Non-ML question - how to map existing algorithms to graph theory and optimization algorithms.
  • How to engineer neuronal properties in physical materials - lots of research in materials science.
  • Open questions
    • Applications
    • Algorithms - theoretical guarantees on SNNs, convergence - SNNs are a superset of ANNs, but just because SNNs can represent ANN functions does not necessarily mean that they can converge to those functions. Increasing the space dimensionality makes the problem harder.
    • Co-design - engineering relevant properties into the hardware to open up exploitation of desired properties.
    • Hardware - materials science and material discovery, architecture desing and fabrication
  • Neuron - can be mapped as an electrical circuit. ANN is modeled by the RC circuit properties of a neuron.
  • Newman computing - CPU, memory, stream bits back and forth. Neuromorphic computing - event-based, more distributed and agglomeration of neurons, no separation of computation and memory

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