Search for: All records

In this work, the effect of rapid thermal annealing (RTA) temperature on the ferroelectric polarization in zirconium-doped hafnium oxide (HZO) was studied. To maximize remnant polarization (2P r ), in-plane tensile stress was induced by tungsten electrodes under optimal RTA temperatures. We observed an increase in 2P r with RTA temperature, likely due to an increased proportion of the polar ferroelectric phase in HZO. The HZO capacitors annealed at 400°C did not exhibit any ferroelectric behavior, whereas the HZO capacitors annealed at 800°C became highly leaky and shorted for voltages above 1 V. On the other hand, annealing at 700 °C produced HZO capacitors with a record-high 2P r of ∼ 64 μ C cm −2  at a relatively high frequency of 111 kHz. These ferroelectric capacitors have also demonstrated impressive endurance and retention characteristics, which will greatly benefit neuromorphic computing applications.

Anthocyanins and proanthocyanins (PAs) are two end products of the flavonoid biosynthesis pathway. They are believed to be synthesized in the endoplasmic reticulum and then sequestered into the vacuole. In Arabidopsis thaliana, TRANSPARENT TESTA 19 (TT19) is necessary for both anthocyanin and PA accumulation. Here, we found that MtGSTF7, a homolog of AtTT19, is essential for anthocyanin accumulation but not required for PA accumulation in Medicago truncatula. MtGSTF7 was induced by the anthocyanin regulator LEGUME ANTHOCYANIN PRODUCTION 1 (LAP1), and its tissue expression pattern correlated with anthocyanin deposition in M. truncatula. Tnt1-insertional mutants of MtGSTF7 lost anthocyanin accumulation in vegetative organs, and introducing a genomic fragment of MtGSTF7 could complement the mutant phenotypes. Additionally, the accumulation of anthocyanins induced by LAP1 was significantly reduced in mtgstf7 mutants. Yeast-one-hybridization and dual-luciferase reporter assays revealed that LAP1 could bind to the MtGSTF7 promoter to activate its expression. Ectopic expression of MtGSTF7 in tt19 mutants could rescue their anthocyanin deficiency, but not their PA defect. Furthermore, PA accumulation was not affected in the mtgstf7 mutants. Taken together, our results show that the mechanism of anthocyanin and PA accumulation in M. truncatula is different from that in A. thaliana, and provide a newmore »target gene for engineering anthocyanins in plants.

« less

Asynchronous event-driven computation and communication using spikes facilitate the realization of spiking neural networks (SNN) to be massively parallel, extremely energy efficient and highly robust on specialized neuromorphic hardware. However, the lack of a unified robust learning algorithm limits the SNN to shallow networks with low accuracies. Artificial neural networks (ANN), however, have the backpropagation algorithm which can utilize gradient descent to train networks which are locally robust universal function approximators. But backpropagation algorithm is neither biologically plausible nor neuromorphic implementation friendly because it requires: 1) separate backward and forward passes, 2) differentiable neurons, 3) high-precision propagated errors, 4) coherent copy of weight matrices at feedforward weights and the backward pass, and 5) non-local weight update. Thus, we propose an approximation of the backpropagation algorithm completely with spiking neurons and extend it to a local weight update rule which resembles a biologically plausible learning rule spike-timing-dependent plasticity (STDP). This will enable error propagation through spiking neurons for a more biologically plausible and neuromorphic implementation friendly backpropagation algorithm for SNNs. We test the proposed algorithm on various traditional and non-traditional benchmarks with competitive results.

In recent years, neuromorphic computing systems (NCS) have gained popularity in accelerating neural network computation because of their high energy efficiency. The known vulnerability of neural networks to adversarial attack, however, raises a severe security concern of NCS. In addition, there are certain application scenarios in which users have limited access to the NCS. In such scenarios, defense technologies that require changing the training methods of the NCS, e.g., adversarial training become impracticable. In this work, we propose AdverQuil – an efficient adversarial detection and alleviation technique for black-box NCS. AdverQuil can identify the adversarial strength of input examples and select the best strategy for NCS to respond to the attack, without changing structure/parameter of the original neural network or its training method. Experimental results show that on MNIST and CIFAR-10 datasets, AdverQuil achieves a high efficiency of 79.5 - 167K image/sec/watt. AdverQuil introduces less than 25% of hardware overhead, and can be combined with various adversarial alleviation techniques to provide a flexible trade-off between hardware cost, energy efficiency and classification accuracy.