In this paper, we propose an Efficient Two-Level I/O Caching Architecture (ETICA) for virtualized platforms that can significantly improve latency, endurance, and cost (in terms of cache size) while preserving the reliability write-pending data blocks. As opposed to previous one-level caching schemes in platforms, our proposed architecture 1) provides two levels by employing both Dynamic Random...

Traditional Deep Neural Network (DNN) security is mostly related to the well-known adversarial input example attack. Recently, another dimension of attack, namely, attack on DNN weight parameters, has been shown be very powerful. As a representative one, Bit-Flip-based Attack (BFA) injects an extremely small amount faults into parameters hijack executing function. Prior works BFA focus <italic ...

In recent years, memristive crossbar-based neuromorphic computing systems (NCS) have obtained extremely high performance in neural network acceleration. However, adversarial attacks and conductance variations of memristors bring reliability challenges to NCS design. First, can fool the pose a serious threat security critical applications. On other hand, device lead degradation accuracy. this pa...

PARyOpt 1 is a Python based implementation of the Bayesian optimization routine designed for remote and asynchronous function evaluations. especially attractive computational due to its low cost footprint as well ability account uncertainties in data. A key challenge efficiently deploy any strategy on distributed computing systems synchronization step, where data from multiple calls assimilated...

Quantum computing has attracted considerable public attention due to its exponential speedup over classical computing. Despite advantages, today's quantum computers intrinsically suffer from noise and are error-prone. To guarantee the high fidelity of execution result a algorithm, it is crucial inform users noises used computer compiled physical circuits. However, an intuitive systematic way ma...

As the machine learning and systems communities strive to achieve higher energy efficiency through custom deep neural network (DNN) accelerators, varied precision or quantization levels, model compression techniques, there is a need for design space exploration frameworks that incorporate quantization-aware processing elements into accelerator while having accurate fast power, performance, area...

Unsupervised pixel-level defective region segmentation is an important task in image-based anomaly detection for various industrial applications. The state-of-the-art methods have their own advantages and limitations: matrix-decomposition-based are robust to noise, but lack complex background image modeling capability; representation-based good at localization, accuracy shape contour extraction...