No abstract

Resistive switching devices were used as technological synapses to learn about the spatial- and temporal-correlated neuron spikes.

Brain-inspired computation can revolutionize information technology by introducing machines capable of recognizing patterns (images, speech, video) and interacting with the external world in a cognitive, humanlike way. Achieving this goal requires first to gain a detailed understanding of the brain operation, and second to identify a scalable microelectronic technology capable of reproducing some of the inherent functions of the human brain, such as the high synaptic connectivity (~104) and the peculiar time-dependent synaptic plasticity. Here we demonstrate unsupervised learning and tracking in a spiking neural network with memristive synapses, where synaptic weights are updated via brain-inspired spike timing dependent plasticity (STDP). The synaptic conductance is updated by the local time-dependent superposition of pre- and post-synaptic spikes within a hybrid one-transistor/one-resistor (1T1R) memristive synapse. Only 2 synaptic states, namely the low resistance state (LRS) and the high resistance state (HRS), are sufficient to learn and recognize patterns. Unsupervised learning of a static pattern and tracking of a dynamic pattern of up to 4 × 4 pixels are demonstrated, paving the way for intelligent hardware technology with up-scaled memristive neural networks.

Conventional digital computers can execute advanced operations by a sequence of elementary Boolean functions of 2 or more bits. As a result, complicated tasks such as solving a linear system or solving a differential equation require a large number of computing steps and an extensive use of memory units to store individual bits. To accelerate the execution of such advanced tasks, in-memory computing with resistive memories provides a promising avenue, thanks to analog data storage and physical computation in the memory. Here, we show that a cross-point array of resistive memory devices can directly solve a system of linear equations, or find the matrix eigenvectors. These operations are completed in just one single step, thanks to the physical computing with Ohm’s and Kirchhoff’s laws, and thanks to the negative feedback connection in the cross-point circuit. Algebraic problems are demonstrated in hardware and applied to classical computing tasks, such as ranking webpages and solving the Schrödinger equation in one step.

A content addressable memory (CAM) is a special form of memory that compares an input search word against all rows of stored words in an array in a highly parallel manner.While supplying a very powerful functionality for many applications in pattern matching and search, CAMs suffer from large area, cost and power consumption, limiting their use.Past improvements have been realized by using non-volatile memristors to replace the staticrandom-access memory in conventional designs, but employ similar schemes based only on binary or ternary states for storage and search. We propose a new analog CAM concept and circuit to overcome these limitations by utilizing the analog conductance tunability of memristors. Our analog CAM stores data within the programmable conductance and can take as input either analog or digital search values. Experimental demonstrations and scaled simulations validated the concept and performance, with analysis showing that our analog CAM can reduce area and power consumption (37×) compared to a digital version. The analog processing nature enables the acceleration of existing CAM applications, but also new computing application areas including fuzzy logic, probabilistic computing, and decision trees.1 arXiv:1907.08177v1 [cs.ET] 18 Jul 2019 success in applications such as network routing 15, 16 , real-time network traffic monitoring 17 , and access control lists (ACL) 18 . While powerful, CAM performance benefits come at the cost of large power and low memory density, limiting modern usage to high cost niche areas that demand high performance. Recent work has shown that utilizing non-volatile memristors (or resistive memory devices) in TCAM circuits reduces area and power 19-27 and provides the flexibility to accelerate powerful finite state machines, particularly for Regular Expression matching used in Network Intrusion Detection Systems 23, 28 . However, nearly all memristor-based CAM designs utilize schemes similar to conventional static random-access-memory (SRAM) designs where the memristor only encodes binary states. The highly tunable analog conductance in memristor devices, with many stable intermediate states is not leveraged 29 .Here, we propose a memristor-based analog CAM that significantly increases data density and reduces operational energy and area for these in-memory processing circuits. Our analog CAM design stores a range of values in each cell using the tunable conductance of memristive devices, and compares an analog input with this stored range to determine a match or mis-match. The concept has been validated with proof-of-concept experiments, as well as simulations to establish performance and scalability. When used to store narrow ranges as discrete levels, our analog CAM can be a direct replacement to digital CAMs, but providing higher memory densities and smaller power consumption. This may enable the use of CAMs for more generic scenarios 30-33 that otherwise struggle with the limited memory densities and high power consumption of conventional CAMs. More importantly, o...