Synaptic Resistor Circuits Based on Al Oxide and Ti Silicide for Concurrent Learning and Signal Processing in Artificial Intelligence Systems.

Neurobiological circuits containing synapses can process signals while learning concurrently in real time. Before an artificial neural network (ANN) can execute a signal-processing program, it must first be programmed by humans or trained with respect to a large and defined data set during learning processes, resulting in significant latency, high power consumption, and poor adaptability to unpredictable changing environments. In this work, a crossbar circuit of synaptic resistors (synstors) is reported, each synstor integrating a Si channel with an Al oxide memory layer and Ti silicide Schottky contacts. Individual synstors are characterized and analyzed to understand their concurrent signal-processing and learning abilities. Without any prior training, synstor circuits concurrently execute signal processing and learning in real time to fly drones toward a target position in an aerodynamically changing environment faster than human controllers, and with learning speed, performance, power consumption, and adaptability to the environment significantly superior to an ANN running on computers. The synstor circuit provides a path to establish power-efficient intelligent systems with real-time learning and adaptability in the capriciously mutable real world.

Synaptic Resistors for Concurrent Inference and Learning with High Energy Efficiency.

The fastest supercomputer, Summit, has a speed comparable to the human brain, but is much less energy-efficient (≈1010 FLOPS W-1 , floating point operations per second per watt) than the brain (≈1015 FLOPS W-1 ). The brain processes and learns from "big data" concurrently via trillions of synapses in parallel analog mode. By contrast, computers execute algorithms on physically separated logic and memory transistors in serial digital mode, which fundamentally restrains computers from handling "big data" efficiently. The existing electronic devices can perform inference with high speeds and energy efficiencies, but they still lack the synaptic functions to facilitate concurrent convolutional inference and correlative learning efficiently like the brain. In this work, synaptic resistors are reported to emulate the analog convolutional signal processing, correlative learning, and nonvolatile memory functions of synapses. By circumventing the fundamental limitations of computers, a synaptic resistor circuit performs speech inference and learning concurrently in parallel analog mode with an energy efficiency of ≈1.6 × 1017 FLOPS W-1 , which is about seven orders of magnitudes higher than that of the Summit supercomputer. Scaled-up synstor circuits could circumvent the fundamental limitations in computers, and facilitate real-time inference and learning from "big data" with high efficiency and speed in intelligent systems.