# Hyperdimensional Computing

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# Introduction

The way the brain works suggests that rather than working with numbers that we are used to, computing with high-dimensional (HD) vectors, e.g., 10,000 bits is more efficient. Computing with HD vectors, referred to as “hypervectors,” offers a general and scalable model of computing as well as well-defined set of arithmetic operations that can enable fast and one-shot learning (no need of back-propagation like in neural networks). Furthermore it is memory-centric with embarrassingly parallel operations and is extremely robust against most failure mechanisms and noise. Such generality, robustness against data uncertainty, and one-shot learning make HD computing a prime candidate for utilization in application domains such as: brain-computer interfaces, biosignal processing (e.g., EEG/ECoG/EMG), robotics, voice/video classification, language recognition, text categorization, scene reasoning, analogical-based reasoning, etc.

Hypervectors are high-dimensional (e.g., 10,000 dimensions), they are (pseudo)random with independent identically distributed components and holographically distributed (i.e., not microcoded). Hypervectors can use various coding: dense or sparse, bipolar or binary and can be combined using arithmetic operations such as multiplication, addition, and permutation. The vectors can be compared for similarity using distance metrics.

## Prerequisites and Focus

If you are an M.S. student, typically there is no special prerequisite. We can redefine and adapt the project based on your skills. However, if you have background in signal processing, VLSI or linear algebra is a super plus! The scope and focus of projects are wide. You can choose to work on:

• Theory of learning systems including HD computing, Hidden Markov Model (HMM), and clustering algorithms
• Exploring various embedded/IoTs applications
• Algorithmic design and optimizations (Matlab/ Python)
• Hardware and digital architecture design
• FPGA prototyping (SystemVerilog/ VHDL)
• ASIC chip and accelerators for low signal-to-noise ratio conditions

# Available Projects

Here, we provide a tiny list of related projects just for your information. The new directions and details of the projects can be adapted based on your interests and skills. Specifically, you can also take a look at the list of publications here to find other active projects we are working on. Please do not hesitate to contact us for more details.

## Extremely Resilient HD Processor

### Short Description

The most important aspect of HD computing, for hardware realization, is its robustness against noise and variations in the computing platforms. Principles of HD computing allows to implement resilient controllers and state machines for extreme noisy conditions. Its tolerance in operating with faulty components and low signal-to-noise ratio (SNR) conditions is achieved by brain-inspired properties of hypervectors: (pseudo)randomness, high-dimensionality, and fully distributed holographic representations.

In this project, your goal would be to design and develop an end-to-end robust HD processor with extremely resilient controller based on principles of HD computing, and measure its resiliency against noisy environment and faulty components.

## Epilepsy Seizure Prediction

### Short Description

Seizure prediction systems hold promise for improving the quality of life for patients with epilepsy that afflicts nearly 1% of the world's population. In this project, your goal would be to develop efficient systems for EEG as well as non-EEG signals to predict an upcoming seizure in a low power device. This covers a wide range of efficient techniques in analog front-end, digital signal processing, and algorithmic aspects. The abilities of HD computing for one-shot, online learning, and interpretable codes come to rescue.

## Online Brain-Computer Interfaces

### Short Description

Noninvasive brain–computer interfaces and neuroprostheses aim to provide a communication and control channel based on the recognition of the subject’s intentions from spatiotemporal neural activity typically recorded by EEG electrodes. What makes it particularly challenging, however, is its susceptibility to errors over time in the recognition of human intentions.

In this project, your goal would be to develop an efficient and fast learning method based on HD computing that replaces the traditional signal processing and classification methods by directly operating with raw data from electrodes in an online fashion.

## Flexible High-Density EMG Hand Gesture Recognition

### Short Description

The surface EMG signals are the superposition of the electrical activity of underneath muscles when contractions occur. Wearable surface EMG devices have a wide range of applications in controlling the upper limb prostheses and hand gesture recognition systems intended for consumer human-machine interaction. High-density EMG electrode array covering the whole arm can ease targeting the most desired muscle locations and cope the issues with sensors misplacement. For robust gesture recognition from such EMG arrays, we rely on brain-inspired HD computing.

In this project, your goal would be to develop an RTL implementation of HD computing for one-shot gesture learning in an ultra low-power device.

## Robot Learning by Demonstration

Image source: Neubert et al, IROS 2016

### Short Description

Robot learning from demonstration is a paradigm for enabling robots to autonomously perform new tasks. HD computing is a nice fit in this area since it naturally enables modeling relation between sensory inputs and actuator outputs of a robot by learning from few demonstrations. In this project, your goal would be to develop algorithms and implementations based on HD computing to enhance a robot to learn from online demonstrations. Further, such HD computing-based paradigm can be coupled to a brain-computer interface device enabling to control a robot by EEG signals from the brain. It has a wonderful application in neuroprosthetics to learn from a patient (see this demonstration at EPFL).