Memristors with Tunable Volatility for Reconfigurable Neuromorphic Computing.

Neuromorphic computing promises an energy-efficient alternative to traditional digital processors in handling data-heavy tasks, primarily driven by the development of both volatile (neuronal) and nonvolatile (synaptic) resistive switches or memristors. However, despite their energy efficiency, memristor-based technologies presently lack functional tunability, thus limiting their competitiveness with arbitrarily programmable (general purpose) digital computers. This work introduces a two-terminal bilayer memristor, which can be tuned among neuronal, synaptic, and hybrid behaviors. The varying behaviors are accessed via facile control over the filament formed within the memristor, enabled by the interplay between the two active ionic species (oxygen vacancies and metal cations). This solution is unlike single-species ion migration employed in most other memristors, which makes their behavior difficult to control. By reconfiguring a single crossbar array of hybrid memristors, two different applications that usually require distinct types of devices are demonstrated - reprogrammable heterogeneous reservoir computing and arbitrary non-Euclidean graph networks. Thus, this work outlines a potential path toward functionally reconfigurable postdigital computers.

Pathogenicity, Host Resistance, and Genetic Diversity of Fusarium Species under Controlled Conditions from Soybean in Canada.

Fusarium spp. are commonly associated with the root rot complex of soybean (Glycine max). Previous surveys identified six common Fusarium species from Manitoba, including F. oxysporum, F. redolens, F. graminearum, F. solani, F. avenaceum, and F. acuminatum. This study aimed to determine their pathogenicity, assess host resistance, and evaluate the genetic diversity of Fusarium spp. isolated from Canada. The pathogenicity of these species was tested on two soybean cultivars, 'Akras' (moderately resistant) and 'B150Y1' (susceptible), under greenhouse conditions. The aggressiveness of the fungal isolates varied, with root rot severities ranging from 1.5 to 3.3 on a 0-4 scale. Subsequently, the six species were used to screen a panel of 20 Canadian soybean cultivars for resistance in a greenhouse. Cluster and principal component analyses were conducted based on the same traits used in the pathogenicity study. Two cultivars, 'P15T46R2' and 'B150Y1', were consistently found to be tolerant to F. oxysporum, F. redolens, F. graminearum, and F. solani. To investigate the incidence and prevalence of Fusarium spp. in Canada, fungi were isolated from 106 soybean fields surveyed across Manitoba, Saskatchewan, Ontario, and Quebec. Eighty-three Fusarium isolates were evaluated based on morphology and with multiple PCR primers, and phylogenetic analyses indicated their diversity across the major soybean production regions of Canada. Overall, this study contributes valuable insights into host resistance and the pathogenicity and genetic diversity of Fusarium spp. in Canadian soybean fields.

Multi-level, forming and filament free, bulk switching trilayer RRAM for neuromorphic computing at the edge.

CMOS-RRAM integration holds great promise for low energy and high throughput neuromorphic computing. However, most RRAM technologies relying on filamentary switching suffer from variations and noise, leading to computational accuracy loss, increased energy consumption, and overhead by expensive program and verify schemes. We developed a filament-free, bulk switching RRAM technology to address these challenges. We systematically engineered a trilayer metal-oxide stack and investigated the switching characteristics of RRAM with varying thicknesses and oxygen vacancy distributions to achieve reliable bulk switching without any filament formation. We demonstrated bulk switching at megaohm regime with high current nonlinearity, up to 100 levels without compliance current. We developed a neuromorphic compute-in-memory platform and showcased edge computing by implementing a spiking neural network for an autonomous navigation/racing task. Our work addresses challenges posed by existing RRAM technologies and paves the way for neuromorphic computing at the edge under strict size, weight, and power constraints.

Note on Coarse Alignment of Gyro-Free Inertial Navigation System.

In this note, the feasibility of initial alignment of a gyro-free inertial navigation system (GF-INS) is investigated. Initial roll and initial pitch are obtained using leveling of conventional INS since centripetal acceleration is very small. The equation for the initial heading cannot be used since the GF inertial measurement unit (IMU) cannot directly measure the Earth rate. A new equation is derived to obtain the initial heading from GF-IMU accelerometer outputs. Initial heading is expressed in the accelerometer outputs of two configurations, which satisfies a specific condition among 15 GF-IMU configurations presented in the literature. The initial heading error to arrangement and accelerometer error is quantitatively analyzed from the initial heading calculation equation of GF-INS and the initial heading error analysis of the general INS. The initial heading error is investigated when gyroscopes are used with GF-IMU. The results show that the initial heading error depends more on the performance of the gyroscope than that of the accelerometer, and the initial heading cannot be obtained within a practical error level by using only GF-IMU, even when an extremely accurate accelerometer is used. Therefore, aiding sensors have to be used in order to have a practical initial heading.

A Neuromorphic Brain Interface based on RRAM Crossbar Arrays for High Throughput Real-time Spike Sorting.

Real-time spike sorting and processing are crucial for closed-loop brain-machine interfaces and neural prosthetics. Recent developments in high-density multi-electrode arrays with hundreds of electrodes have enabled simultaneous recordings of spikes from a large number of neurons. However, the high channel count imposes stringent demands on real-time spike sorting hardware regarding data transmission bandwidth and computation complexity. Thus, it is necessary to develop a specialized real-time hardware that can sort neural spikes on the fly with high throughputs while consuming minimal power. Here, we present a real-time, low latency spike sorting processor that utilizes high-density CuOx resistive crossbars to implement in-memory spike sorting in a massively parallel manner. We developed a fabrication process which is compatible with CMOS BEOL integration. We extensively characterized switching characteristics and statistical variations of the CuOx memory devices. In order to implement spike sorting with crossbar arrays, we developed a template matching-based spike sorting algorithm that can be directly mapped onto RRAM crossbars. By using synthetic and in vivo recordings of extracellular spikes, we experimentally demonstrated energy efficient spike sorting with high accuracy. Our neuromorphic interface offers substantial improvements in area (~1000× less area), power (~200× less power), and latency (4.8µs latency for sorting 100 channels) for real-time spike sorting compared to other hardware implementations based on FPGAs and microcontrollers.

Impacts of Visualizations on Decoy Effects.

The decoy effect is a well-known, intriguing decision-making bias that is often exploited by marketing practitioners to steer consumers towards a desired purchase outcome. It demonstrates that an inclusion of an alternative in the choice set can alter one's preference among the other choices. Although this decoy effect has been universally observed in the real world and also studied by many economists and psychologists, little is known about how to mitigate the decoy effect and help consumers make informed decisions. In this study, we conducted two experiments: a quantitative experiment with crowdsourcing and a qualitative interview study-first, the crowdsourcing experiment to see if visual interfaces can help alleviate this cognitive bias. Four types of visualizations, one-sided bar chart, two-sided bar charts, scatterplots, and parallel-coordinate plots, were evaluated with four different types of scenarios. The results demonstrated that the two types of bar charts were effective in decreasing the decoy effect. Second, we conducted a semi-structured interview to gain a deeper understanding of the decision-making strategies while making a choice. We believe that the results have an implication on showing how visualizations can have an impact on the decision-making process in our everyday life.

Energy-efficient Mott activation neuron for full-hardware implementation of neural networks.

To circumvent the von Neumann bottleneck, substantial progress has been made towards in-memory computing with synaptic devices. However, compact nanodevices implementing non-linear activation functions are required for efficient full-hardware implementation of deep neural networks. Here, we present an energy-efficient and compact Mott activation neuron based on vanadium dioxide and its successful integration with a conductive bridge random access memory (CBRAM) crossbar array in hardware. The Mott activation neuron implements the rectified linear unit function in the analogue domain. The neuron devices consume substantially less energy and occupy two orders of magnitude smaller area than those of analogue complementary metal-oxide semiconductor implementations. The LeNet-5 network with Mott activation neurons achieves 98.38% accuracy on the MNIST dataset, close to the ideal software accuracy. We perform large-scale image edge detection using the Mott activation neurons integrated with a CBRAM crossbar array. Our findings provide a solution towards large-scale, highly parallel and energy-efficient in-memory computing systems for neural networks.

Genome-wide identification and characterization of NBS-encoding genes in Raphanus sativus L. and their roles related to Fusarium oxysporum resistance.

BACKGROUND: The nucleotide-binding site-leucine-rich repeat (NBS-LRR) genes are important for plant development and disease resistance. Although genome-wide studies of NBS-encoding genes have been performed in several species, the evolution, structure, expression, and function of these genes remain unknown in radish (Raphanus sativus L.). A recently released draft R. sativus L. reference genome has facilitated the genome-wide identification and characterization of NBS-encoding genes in radish. RESULTS: A total of 225 NBS-encoding genes were identified in the radish genome based on the essential NB-ARC domain through HMM search and Pfam database, with 202 mapped onto nine chromosomes and the remaining 23 localized on different scaffolds. According to a gene structure analysis, we identified 99 NBS-LRR-type genes and 126 partial NBS-encoding genes. Additionally, 80 and 19 genes respectively encoded an N-terminal Toll/interleukin-like domain and a coiled-coil domain. Furthermore, 72% of the 202 NBS-encoding genes were grouped in 48 clusters distributed in 24 crucifer blocks on chromosomes. The U block on chromosomes R02, R04, and R08 had the most NBS-encoding genes (48), followed by the R (24), D (23), E (23), and F (17) blocks. These clusters were mostly homogeneous, containing NBS-encoding genes derived from a recent common ancestor. Tandem (15 events) and segmental (20 events) duplications were revealed in the NBS family. Comparative evolutionary analyses of orthologous genes among Arabidopsis thaliana, Brassica rapa, and Brassica oleracea reflected the importance of the NBS-LRR gene family during evolution. Moreover, examinations of cis-elements identified 70 major elements involved in responses to methyl jasmonate, abscisic acid, auxin, and salicylic acid. According to RNA-seq expression analyses, 75 NBS-encoding genes contributed to the resistance of radish to Fusarium wilt. A quantitative real-time PCR analysis revealed that RsTNL03 (Rs093020) and RsTNL09 (Rs042580) expression positively regulates radish resistance to Fusarium oxysporum, in contrast to the negative regulatory role for RsTNL06 (Rs053740). CONCLUSIONS: The NBS-encoding gene structures, tandem and segmental duplications, synteny, and expression profiles in radish were elucidated for the first time and compared with those of other Brassicaceae family members (A. thaliana, B. oleracea, and B. rapa) to clarify the evolution of the NBS gene family. These results may be useful for functionally characterizing NBS-encoding genes in radish.

A Soft-Pruning Method Applied During Training of Spiking Neural Networks for In-memory Computing Applications.

Inspired from the computational efficiency of the biological brain, spiking neural networks (SNNs) emulate biological neural networks, neural codes, dynamics, and circuitry. SNNs show great potential for the implementation of unsupervised learning using in-memory computing. Here, we report an algorithmic optimization that improves energy efficiency of online learning with SNNs on emerging non-volatile memory (eNVM) devices. We develop a pruning method for SNNs by exploiting the output firing characteristics of neurons. Our pruning method can be applied during network training, which is different from previous approaches in the literature that employ pruning on already-trained networks. This approach prevents unnecessary updates of network parameters during training. This algorithmic optimization can complement the energy efficiency of eNVM technology, which offers a unique in-memory computing platform for the parallelization of neural network operations. Our SNN maintains ~90% classification accuracy on the MNIST dataset with up to ~75% pruning, significantly reducing the number of weight updates. The SNN and pruning scheme developed in this work can pave the way toward applications of eNVM based neuro-inspired systems for energy efficient online learning in low power applications.

Neuroinspired unsupervised learning and pruning with subquantum CBRAM arrays.

Resistive RAM crossbar arrays offer an attractive solution to minimize off-chip data transfer and parallelize on-chip computations for neural networks. Here, we report a hardware/software co-design approach based on low energy subquantum conductive bridging RAM (CBRAM®) devices and a network pruning technique to reduce network level energy consumption. First, we demonstrate low energy subquantum CBRAM devices exhibiting gradual switching characteristics important for implementing weight updates in hardware during unsupervised learning. Then we develop a network pruning algorithm that can be employed during training, different from previous network pruning approaches applied for inference only. Using a 512 kbit subquantum CBRAM array, we experimentally demonstrate high recognition accuracy on the MNIST dataset for digital implementation of unsupervised learning. Our hardware/software co-design approach can pave the way towards resistive memory based neuro-inspired systems that can autonomously learn and process information in power-limited settings.