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

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

Multi-level, Forming Free, Bulk Switching Trilayer RRAM for Neuromorphic Computing at the Edge

Resistive memory-based reconfigurable systems constructed by CMOS-RRAM integration hold 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. Low ON-state resistance of filamentary RRAM devices further increases the energy consumption due to high-current read and write operations, and limits the array size and parallel multiply & accumulate operations. High-forming voltages needed for filamentary RRAM are not compatible with advanced CMOS technology nodes. To address all these challenges, we developed a forming-free and bulk switching RRAM technology based on a trilayer metal-oxide stack. We systematically engineered a trilayer metal-oxide RRAM stack and investigated the switching characteristics of RRAM devices with varying thicknesses and oxygen vacancy distributions across the trilayer to achieve reliable bulk switching without any filament formation. We demonstrated bulk switching operation at megaohm regime with high current nonlinearity and programmed up to 100 levels without compliance current. We developed a neuromorphic compute-in-memory platform based on trilayer bulk RRAM crossbars by combining energy-efficient switched-capacitor voltage sensing circuits with differential encoding of weights to experimentally demonstrate high-accuracy matrix-vector multiplication. We showcased the computational capability of bulk RRAM crossbars by implementing a spiking neural network model 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

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

Suspended scattering particles in motion using OCT angiography in branch retinal vein occlusion disease cases with cystoid macular edema

Abstract We aimed to investigate the clinical implication of suspended scattering particles in motion (SSPiM) using optical coherence tomography angiography (OCTA) among branch retinal vein occlusion disease (BRVO) cases with macular edema (ME). Medical records of BRVO patients were reviewed. Central retinal thickness (CRT), ME type, and cyst size on optical coherence tomography images were evaluated before and after intravitreal bevacizumab injection. Nonperfusion area, SSPiM, and microvascular abnormalities in OCTA images were evaluated using a Heidelberg machine. SSPiM was identified in 24 of 56 cases. There were no differences in baseline characteristics between groups with and without SSPiM. Disease duration, disease-free duration, previous injection number, microaneurysms in the superficial vascular complex, and microaneurysms in the deep vascular complex (DVC) (p = 0.003, 0.013, 0.028, 0.003, < 0.001, respectively) differed significantly between the two groups. After multivariate logistic analysis, microaneurysms in the DVC were the only different factor between the two groups (odds ratio [OR]: 0.091; p = 0.001). Furthermore, SSPiM in the DVC (OR 10.908; p = 0.002) and nonperfusion grade (OR 0.039; p < 0.001) were significantly associated with cyst response after intravitreal injection. SSPiM may be correlated with microaneurysms in the DVC and a poor anatomical response after intravitreal injection