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Enabling Training of Neural Networks on Noisy Hardware


Abstract:  "Deep neural networks (DNNs) are typically trained using the conventional stochastic gradient descent (SGD) algorithm. However, SGD performs poorly when applied to train networks on non-ideal analog hardware composed of resistive device arrays with non-symmetric conductance modulation characteristics. Recently we proposed a new algorithm, the Tiki-Taka algorithm, that overcomes t... » read more

Power/Performance Bits: Nov. 8


Molecular memristor Researchers from National University of Singapore, Indian Association for the Cultivation of Science, University of Limerick, Texas A&M University, and Hewlett Packard Enterprise discovered a molecular memristor for brain-inspired computing. The molecule uses natural asymmetry in its metal-organic bonds to switch between different states, which allows it to perform u... » read more

Standards for the Characterization of Endurance in Resistive Switching Devices


Abstract "Resistive switching (RS) devices are emerging electronic components that could have applications in multiple types of integrated circuits, including electronic memories, true random number generators, radiofrequency switches, neuromorphic vision sensors, and artificial neural networks. The main factor hindering the massive employment of RS devices in commercial circuits is related to... » read more

Compute-In Memory Accelerators Up-End Network Design Tradeoffs


An explosion in the amount of data, coupled with the negative impact on performance and power for moving that data, is rekindling interest around in-memory processing as an alternative to moving data back and forth between the memory and the processor. Compute-in-memory (CIM) arrays based on either conventional memory elements like DRAM and NAND flash, as well as emerging non-volatile memori... » read more

Manufacturing Bits: April 21


Memristors reappear The University of Massachusetts Amherst has taken a step towards of the realization of neuromorphic computing--it has devised bio-voltage memristors based on protein nanowires. In neuromorphic computing, the idea is to bring the memory closer to the processing tasks to speed up a system. For this, the industry is attempting to replicate the brain in silicon. The goal is ... » read more

System Bits: May 8


Unlocking the brain Stanford University researchers recently reminded that for years, the people developing artificial intelligence drew inspiration from what was known about the human brain, and now AI is starting to return the favor: while not explicitly designed to do so, certain AI systems seem to mimic our brains’ inner workings more closely than previously thought. [caption id="attach... » read more

What If We Had Bi-Directional RRAM?


The ideal memristor device for neuromorphic computing would have linear and symmetric resistance behavior. Resistance would both increase and decrease gradually, allowing a direct correlation between the number of programming pulses and the resistance value. Real world RRAM devices, however, generally do not have these characteristics. In filamentary RRAM devices, the RESET operation can raise ... » read more

3D Neuromorphic Architectures


Matrix multiplication is a critical operation in conventional neural networks. Each node of the network receives an input signal, multiplies it by some predetermined weight, and passes the result to the next layer of nodes. While the nature of the signal, the method used to determine the weights, and the desired result will all depend on the specific application, the computational task is simpl... » read more

What Happened To ReRAM?


Resistive RAM (ReRAM), one of a handful of next-generation memories under development, is finally gaining traction after years of setbacks. Fujitsu and Panasonic are jointly ramping up a second-generation ReRAM device. In addition, Crossbar is sampling a 40nm ReRAM technology, which is being made on a foundry basis by China’s SMIC. And not to be outdone, TSMC and UMC recently put ReRAM on ... » read more

System Bits: Nov. 15


Revolutionizing sports via AI and computer vision A new technology developed by PlayfulVision — an EPFL startup — will be used in all NBA games in the United States starting next year to records all aspects of sporting events for subsequent analysis in augmented reality. Will artificial intelligence and computer vision revolutionize the sports industry? PlayfulVision’s approach uses ... » read more

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