Modern computing systems are limited by the need to move data between the processing units and the memory (“memory wall”). We developed a unit that combines the data processing and storage using the same physical cells using memristive devices. This unit, called mMPU, can execute numerous logical operations simultaneously, offering an energy efficient, high performance machine that is backward compatible with standard computer architectures. The mMPU is especially efficient for applications such as genomics, databases, image processing, DNN and BNN.
Data converters (analog to digital and digital to analog) are ubiquities in modern electronic devices and connect the real world with digital computing systems. These converters suffer from the speed-accuracy-power tradeoff. We use neuromorphic computing to build data converters that can be trained to adjust to different applications and environmental changes and by that achieve a better figure-of-merit compared to standard data converters.
We use emerging memristive technologies to design circuits and systems that accelerate deep neural networks, including their training. Our recent work has shown how to accelerate vanilla gradient descent and gradient descent with momentum using memristors. Our proposed circuits rely on using memristors to both compute and store the weights.