Passively-integrated memristors are the most prospective candidates for designing high-speed, energy-efficient, and compact neuromorphic circuits. Despite all the promising properties, experimental demonstrations of passive memristive crossbars have been limited to circuits with few thousands of devices until now, which stems from the strict uniformity requirements on theIVcharacteristics of memristors. This paper expands upon this vital challenge and investigates how uniformity impacts the computing accuracy of analog memristive circuits, focusing on neuromorphic applications. Specifically, the paper explores the tradeoffs between computing accuracy, crossbar size, switching threshold variations, and target precision. All-embracing simulations of matrix multipliers and deep neural networks on CIFAR-10 and ImageNet datasets have been carried out to evaluate the role of uniformity on the accuracy of computing systems. Further, we study three post-fabrication methods that increase the accuracy of nonuniform 0T1R neuromorphic circuits: hardware-aware training, improved tuning algorithm, and switching threshold modification. The application of these techniques allows us to implement advanced deep neural networks with almost no accuracy drop, using current state-of-the-art analog 0T1R technology.

We exploit randomness in static I-V characteristics and reconfigurability of embedded flash memories to design very efficient physically unclonable function. Leakage current and subthreshold slope variations, nonlinearity, nondeterministic tuning error, and sneak path current in the redesigned commercial flash memory arrays are exploited to create a unique digital fingerprint. A time-multiplexed architecture is designed to enhance the security and expand the challenge-response pair space to 10211. Experimental results demonstrate 50.3% average uniformity, 49.99% average diffuseness, and native <5% bit error rate. The analysis of the measured data also shows strong resilience against machine learning attacks and possibility for extremely energy efficient, 0.56 pJ/b operation.

We have designed and fabricated a neuromorphic accelerator chip which features 180-nm CMOS circuitry and 2.4-million 0.04-μm2-footprint Al2O3/TiO2-xmemristors. Memristors were passively integrated on top of the CMOS wafer into 48×48 crossbar circuits, with a total of 1032 crossbars on a chip. Each memristive crossbar is accessed via on-chip CMOS interface circuits which are controlled by a custom FPGA board. The whole system is designed to implement a variety of deep neural networks by performing vector-by-matrix multiplication, a core operation of any neural network, with a memristive crossbar circuit in analog domain. In this this paper we provide details of the on-chip CMOS circuits and the FPGA board, both of which have already been successfully tested. We also briefly discuss integration results and the on-going work.

Emerging non-volatile reduction-oxidation (redox)-based resistive switching memories (ReRAMs) exhibit a unique set of characteristics that make them promising candidates for the next generation of low-cost, low-power, tiny, and secure physical unclonable functions (PUFs). Their underlying stochastic ionic conduction behavior, intrinsic nonlinear current-voltage characteristics, and their well-known nano-fabrication process variability might normally be considered disadvantageous ReRAM features. However, using a combination of a novel architecture and special peripheral circuitry, this paper exploits these non-idealities in a physical one-way function, nonlinear resistive PUF, potentially applicable to a variety of cyber-physical security applications. We experimentally verify the performance of valency change mechanism (VCM)-based ReRAM in nano-fabricated crossbar arrays across multiple dies and runs. In addition to supporting a massive pool of challenge-response pairs (CRPs), using a combination of experiment and simulation our proposed PUF exhibits a reliability of 98.67%, a uniqueness of 49.85%, a diffuseness of 49.86%, a uniformity of 47.28%, and a bit-aliasing of 47.48%.