Brain-like associative learning using a nanoscale non-volatile phase change synaptic device array
This work addresses the problem of building brain-inspired computing hardware for AI applications, representing an incremental step by moving from simulations to experimental array-level demonstrations.
The authors tackled the challenge of implementing brain-like associative learning at the network level using nanoscale phase change synaptic devices, demonstrating through experiments that a grid of these devices can store and recall patterns associatively while being robust to device variations, with energy consumption decreasing significantly for lower variation tolerance.
Recent advances in neuroscience together with nanoscale electronic device technology have resulted in huge interests in realizing brain-like computing hardwares using emerging nanoscale memory devices as synaptic elements. Although there has been experimental work that demonstrated the operation of nanoscale synaptic element at the single device level, network level studies have been limited to simulations. In this work, we demonstrate, using experiments, array level associative learning using phase change synaptic devices connected in a grid like configuration similar to the organization of the biological brain. Implementing Hebbian learning with phase change memory cells, the synaptic grid was able to store presented patterns and recall missing patterns in an associative brain-like fashion. We found that the system is robust to device variations, and large variations in cell resistance states can be accommodated by increasing the number of training epochs. We illustrated the tradeoff between variation tolerance of the network and the overall energy consumption, and found that energy consumption is decreased significantly for lower variation tolerance.