Publications by authors named "Alice Mizrahi"
Introduction: Acute kidney injury (AKI) during critical illness increases the risk of subsequent chronic kidney disease. Guidelines recommend inpatient nephrology assessment and review at 3 months.
Objectives: To quantify the prevalence and predictors of inpatient and outpatient nephrology follow-up of AKI patients admitted to critical care areas within a tertiary hospital.
Superparamagnetic tunnel junctions (SMTJs) have emerged as a competitive, realistic nanotechnology to support novel forms of stochastic computation in CMOS-compatible platforms. One of their applications is to generate random bitstreams suitable for use in stochastic computing implementations. We describe a method for digitally programmable bitstream generation based on pre-charge sense amplifiers.
View Article and Find Full Text PDFFinding the shortest path in a graph has applications in a wide range of optimization problems. However, algorithmic methods scale with the size of the graph in terms of time and energy. We propose a method to solve the shortest-path problem using circuits of nanodevices called memristors and validate it on graphs of different sizes and topologies.
View Article and Find Full Text PDFArticle Synopsis
- - The text discusses how population coding theory in neuroscience can enhance fault-tolerant information processing, which could be applied to improve computing using small, imperfect devices.
- - It highlights the limitations of traditional CMOS technology in terms of area and energy needs, suggesting nanoscale magnetic tunnel junctions as a more efficient alternative.
- - Experimental results show that a setup of nine junctions can effectively achieve complex tasks like generating cursive letters, paving the way for hybrid systems that can learn and perform resilient computations with lower resource consumption.
The brain, which uses redundancy and continuous learning to overcome the unreliability of its components, provides a promising path to building computing systems that are robust to the unreliability of their constituent nanodevices. In this work, we illustrate this path by a computing system based on population coding with magnetic tunnel junctions that implement both neurons and synaptic weights. We show that equipping such a system with continuous learning enables it to recover from the loss of neurons and makes it possible to use unreliable synaptic weights ( low energy barrier magnetic memories).
View Article and Find Full Text PDFArticle Synopsis
- One major challenge in making smaller magnetic memories is keeping them stable, as tiny magnetic volumes can start to switch spontaneously due to thermal energy.
- Superparamagnetic nanomagnets are usually seen as unusable because of this instability, but they can actually oscillate naturally without external energy.
- The study demonstrates that these nanomagnets can synchronize their oscillations through electrical noise, enabling the potential for ultra-low power computing with minimal energy costs.