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Katherine Derbyshire

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Katherine Derbyshire is a technical editor at Semiconductor Engineering.

Author's Latest Posts

The Growing Materials Challenge
By Katherine Derbyshire - 11 Jun, 2018 - Comments: 0

By Katherine Derbyshire & Ed Sperling Materials have emerged as a growing challenge across the semiconductor supply chain, as chips continue to scale, or as they are utilized in new devices such as sensors for AI or machine learning systems. Engineered materials are no longer optional at advanced nodes. They are now a requirement, and the amount of new material content in chips contin... » read more

Integrating Memristors For Neuromorphic Computing
By Katherine Derbyshire - 16 May, 2018 - Comments: 1

Much of the current research on neuromorphic computing focuses on the use of non-volatile memory arrays as a compute-in-memory component for artificial neural networks (ANNs). By using Ohm’s Law to apply stored weights to incoming signals, and Kirchoff’s Laws to sum up the results, memristor arrays can accelerate the many multiply-accumulate steps in ANN algorithms. ANNs are being dep... » read more

Preparing For AI
By Katherine Derbyshire - 03 May, 2018 - Comments: 0

Suppose an autonomous car is coming up an on-ramp onto a bridge. The ramp is fine, but the bridge is icy, and there’s an overturned bus full of children blocking several lanes. Children are evacuating through the windows and milling around on the pavement. There isn’t time to stop, even with the better-than-human reaction time an autonomous car might have. Swerving to one side might send... » read more

How The Brain Saves Energy By Doing Less
By Katherine Derbyshire - 15 Mar, 2018 - Comments: 0

One of the arguments for neuromorphic computing is the efficiency of the human brain relative to conventional computers. By looking at how the brain works, this argument contends, we can design systems that accomplish more with less power. However, as Mireille Conrad and others at the University of Geneva pointed out in work presented at December's IEEE Electron Device Meeting, the brain... » read more

What If We Had Bi-Directional RRAM?
By Katherine Derbyshire - 15 Feb, 2018 - Comments: 0

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

What’s Next In Neuromorphic Computing
By Katherine Derbyshire - 15 Feb, 2018 - Comments: 2

To integrate devices into functioning systems, it's necessary to consider what those systems are actually supposed to do. Regardless of the application, [getkc id="305" kc_name="machine learning"] tasks involve a training phase and an inference phase. In the training phase, the system is presented with a large dataset and learns how to "correctly" analyze it. In supervised learning, the data... » read more

How Good Is 95% Accuracy?
By Katherine Derbyshire - 28 Dec, 2017 - Comments: 0

Conventional, deterministic computers don’t make mistakes. They execute a predictable series of computations in response to any given input. The input might be mistaken. The logic behind the operations that are performed might be flawed. But the computer will always do exactly what it has been told to do. When unexpected results occur, they can be attributed to the programmer, the system manu... » read more

3D Neuromorphic Architectures
By Katherine Derbyshire - 11 Dec, 2017 - Comments: 1

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

Toward Neuromorphic Designs
By Katherine Derbyshire - 25 Oct, 2017 - Comments: 1

Part one of this series considered the mechanisms of learning and memory in biological brains. Each neuron has many fibers, which connect to adjacent neurons at synapses. The concentration of ions such as potassium and calcium inside the cell is different from the concentration outside. The cellular membrane thus serves as a capacitor. When a stimulus is received, the neuron releases neur... » read more

Terminology Beyond von Neumann
By Katherine Derbyshire - 21 Sep, 2017 - Comments: 0

Neural networks. Neuromorphic computing. Non-von Neumann architectures. As I’ve been researching my series on neuromorphic computing, I’ve encountered a lot of new terminology. It hasn’t always been easy to figure out exactly what’s being discussed. This explainer attempts to both clarify the terms used in my own articles and to help others sort through the rapidly growing literature in... » read more

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