Neural Networks Circuits Based On Resistive Memories
Carlo Reita
CEA Grenoble, FR
ABSTRACT
In recent years, the field of Neural Networks has found a new golden age after nearly twenty years of lessened interest. Under the heading of Artificial Intelligence (AI) a large number of Deep Neural Netwooks (DNNs) have recently found application in image processing, management of information in large databases, decision aids, natural language recognition, etc. Most of these applications rely on algorithms that run on standard computing systems and sometimes make use of specific accelerators like Graphic Processor Units (GPUs) or dedicated highly parallel processors. In effect, a common operation in all NN algorithms is the scalar product of two vectors and its optimisation is of paramount importance to reduce computational time and energy. In particular, the energy element is relevant for all embedded applications that cannot rely on cooling and/or unlimited power supply. The availability of resistive memories, with their unique capability of both storing computational values and of performing analog multiplication by the use of ohm’s law, allows new circuit architectures where the latency, bandwidth limitations and power consumption issues associated to the use of conventional SRAM, DRAM and Flash memories can be greatly improved upon. In the presentation, some examples of advantageous use of resistive memories in NN circuits will be shown and some of their peculiarities will be discussed.
