Abstract: Matrix computation is ubiquitous in modern scientific and engineering fields. Due to the high computational complexity in conventional digital computers, matrix computation represents a ...
Abstract: In-memory computing is an emerging computing paradigm that overcomes the limitations of exiting Von-Neumann computing architectures such as the memory-wall bottleneck. In such paradigm, the ...
I was recently mulling on FPGA implementations of short length Fast Fourier Transforms (roughly N=30 to 100) at work. My dilemma was I needed a potentially arbitrary number of these in a scalable ...
Everything on a computer is at its core a binary number, since computers do everything with bits that represent 0 and 1. In order to have a file that is "plain text", so human readable with minimal ...
Deep learning has become a widespread tool in both science and industry. However, continued progress is hampered by the rapid growth in energy costs of ever-larger deep neural networks. Optical neural ...
In recent years, multivariate pattern analysis (MVPA) has been hugely beneficial for cognitive neuroscience by making new experiment designs possible and by increasing the inferential power of ...
This tutorial shows step-by-step how to create an R and Python package which performs a naive matrix multiplication which uses just three for-loops. However, at each step more and more efficient low ...
DynaSim is an open-source MATLAB/GNU Octave toolbox for rapid prototyping of neural models and batch simulation management. It is designed to speed up and simplify the process of generating, sharing, ...
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