How The Brain Saves Energy By Doing Less

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?

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

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

New Nodes, Materials, Memories

Ellie Yieh, vice president and general manager of Advanced Product Technology Development at [getentity id="22817" e_name="Applied Materials"], and head of the company's Maydan Technology Center, sat down with Semiconductor Engineering to talk about challenges, changes and solutions at advanced nodes and with new applications. What follows are excerpts of that conversation. SE: How far can w... » read more

System Bits: Jan. 30

Lab-in-the-cloud Although Internet-connected smart devices have penetrated numerous industries and private homes, the technological phenomenon has left the research lab largely untouched, according to MIT researchers. Spreadsheets, individual software programs, and even pens and paper remain standard tools for recording and sharing data in academic and industry labs — until now. TetraScie... » read more

System Bits: Jan. 23

Artificial synapse for “brain-on-a-chip” portable AI devices In the emerging field of neuromorphic computing, researchers are attempting to design computer chips that work like the human brain, which, instead of carrying out computations based on binary, on/off signaling like digital chips do today, the elements of a brain-on-a-chip would work in an analog fashion, exchanging a gradient of... » 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

Toward Neuromorphic Designs

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

Verifying AI, Machine Learning

[getperson id="11306" comment="Raik Brinkmann"], president and CEO of [getentity id="22395" e_name="OneSpin Solutions"], sat down to talk about artificial intelligence, machine learning, and neuromorphic chips. What follows are excerpts of that conversation. SE: What's changing in [getkc id="305" kc_name="machine learning"]? Brinkmann: There’s a real push toward computing at the edge. ... » read more

Terminology Beyond von Neumann

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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