EvAn: Neuromorphic Event-Based Sparse Anomaly Detection

Flash-Based Computing-in-Memory Architecture to Implement High-Precision Sparse Coding

To address the concerns with power consumption and processing efficiency in big-size data processing, sparse coding in computing-in-memory (CIM) architectures is gaining much more attention. Here, a novel Flash-based CIM architecture is proposed to implement large-scale sparse coding, wherein various matrix weight training algorithms are verified. Then, with further optimizations of mapping methods and initialization conditions, the variation-sensitive training (VST) algorithm is designed to enhance the processing efficiency and accuracy of the applications of image reconstructions. Based on the comprehensive characterizations observed when considering the impacts of array variations, the experiment demonstrated that the trained dictionary could successfully reconstruct the images in a 55 nm flash memory array based on the proposed architecture, irrespective of current variations. The results indicate the feasibility of using Flash-based CIM architectures to implement high-precision sparse coding in a wide range of applications.

Achieving nanoscale precision using neuromorphic localization microscopy

Neuromorphic cameras are a new class of dynamic-vision-inspired sensors that encode the rate of change of intensity as events. They can asynchronously record intensity changes as spikes, independent of the other pixels in the receptive field, resulting in sparse measurements. This recording of such sparse events makes them ideal for imaging dynamic processes, such as the stochastic emission of isolated single molecules. Here we show the application of neuromorphic detection to localize nanoscale fluorescent objects below the diffraction limit, with a precision below 20 nm. We demonstrate a combination of neuromorphic detection with segmentation and deep learning approaches to localize and track fluorescent particles below 50 nm with millisecond temporal resolution. Furthermore, we show that combining information from events resulting from the rate of change of intensities improves the classical limit of centroid estimation of single fluorescent objects by nearly a factor of two. Additionally, we validate that using post-processed data from the neuromorphic detector at defined windows of temporal integration allows a better evaluation of the fractalized diffusion of single particle trajectories. Our observations and analysis is useful for event sensing by nonlinear neuromorphic devices to ameliorate real-time particle localization approaches at the nanoscale.