Projects:2018s1-121 In-Memory Semantic Processing Using Hyperdimensional Computing
Project Team
Hanchao Guo
Tait Moseley
Supervisor and Co-advisor
Branden Phillips and Peng Wang
Introduction
In hyperdimensional computing, long binary vectors are used to encode knowledge. The relationship between facts can be captured using correlations between their binary encodings. Specially designed memory hardware can then perform operations, such as searches for relevant information, in parallel, in the memory. This is one way to overcome the well known von Neumann bottleneck of conventional computers, in which memory can only be searched sequentially. Hyperdimenional computing is a relatively new idea. A first aim of this project is to develop a case study example, simulated in software, that demonstrates its use in a practical application. The second aim of the project is to investigate hardware designs for memory circuits to perform hypervector operations in memory. These circuits may use emerging technologies such as resistive RAMs (RRAMs). Hardware to automate the spreading activation function is of particular interest.
WSD
RRAM
RRAM is an emerging technology, which has overall non-linear resistive interval. RRAM model can be generally summarised into two resistive mode, high resistive mode and low resistive mode from explicit voltage intervals.
