RDF data evolution: automatic detection and semantic representation of changes

Pattern matching algorithms, which may be realized via associative memories, require further improvements in both accuracy and power consumption to achieve more widespread use in real-world applications. In this work we utilized a memristive crossbar to combine computation and memory in an approximate Hamming distance computing architecture for an associative memory. For classifying handwritten digits from the MNIST data-set, we showed that using the Hamming distance rather than the traditional dot product increased accuracy, and decreased power consumption by 100×. Moreover, we showed that we can trade-off accuracy to save additional power or vice-versa by adjusting the input voltage. This trade-off may be adjusted for the architecture depending on its application. Our architecture consumed 200× less power than other previously proposed Hamming distance associative memory architectures, due to the use of memristive devices, and is 256× faster than prior work due to our leveraging of in-memory computation. Improved associative memories should prove useful for GPUs, handwriting recognition, DNA sequence matching, object detection, and other applications.