AI Hardware

IBM Scientists Demonstrate Mixed-Precision In-Memory Computing for the First Time; Hybrid Design for AI Hardware

Today, we are entering the era of cognitive computing, which holds great promise in deriving intelligence and knowledge from huge volumes of data. One of the biggest challenges in using these huge volumes of data is the fundamental design of today’s computers, which are based on the von Neumann architecture, requiring data to be shuttled […]

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IBM Sets Tera-scale Machine Learning Benchmark Record with POWER9 and NVIDIA GPUs; Available Soon in PowerAI

Today, at IBM THINK in Las Vegas, we are reporting a breakthrough in AI performance using new software and algorithms on optimized hardware, including POWER9 with NVIDIA® V100™ GPUs. In a newly published benchmark, using an online advertising dataset released by Criteo Labs with over 4 billion training examples, we train a logistic regression classifier […]

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Deep Learning Training Times Get Significant Reduction

IBM researchers developed a novel compression algorithm that could significantly improve training times for deep learning models in large-scale AI systems.

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The future of hardware is AI

To make great strides in AI, hardware must change. Starting with GPUs, and then evolving to analog devices, and then fault tolerant quantum computers.

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IBM Scientists Demonstrate In-memory Computing with 1 Million Devices for Applications in AI

“In-memory computing” or “computational memory” is an emerging concept that uses the physical properties of memory devices for both storing and processing information. This is counter to current von Neumann systems and devices, such as standard desktop computers, laptops and even cellphones, which shuttle data back and forth between memory and the computing unit, thus […]

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IBM Research achieves record deep learning performance with new software technology

IBM Research publishes close to ideal scaling with new distributed deep learning software which achieved record communication overhead.

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IBM demos event-based gesture recognition using a brain-inspired chip at CVPR 2017

Event-based computation is a biologically-inspired paradigm for representing data as asynchronous events, much like neuron spikes in the brain. The Brain-Inspired Computing group at IBM Research – Almaden has built the first gesture-recognition system implemented end-to-end on event-based hardware. Combining the IBM TrueNorth neurosynaptic processor with an iniLabs Dynamic Vision Sensor (DVS), we trained a […]

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Grad student wins IBM Fellowship to Mimic Brain Architecture

S. R. Nandakumar, a graduate student in electrical engineering, has won a coveted IBM Ph.D. fellowship to support his work on computer systems that mimic the architecture of the human brain. He is currently interning at IBM’s Zurich Lab and we had the chance to ask him a few questions. Q. Last August IBM scientists published […]

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The brain’s architecture, efficiency… on a chip

2016 was a big year for brain-inspired computing. My team and I proved in our paper “Convolutional networks for fast, energy-efficient neuromorphic computing” that the value of this breakthrough is that it can perform neural network inference at unprecedented ultra-low energy consumption. Simply stated, our TrueNorth chip’s non-von Neumann architecture mimics the brain’s neural architecture […]

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Deep learning inference possible in embedded systems thanks to TrueNorth

Scientists at IBM Research – Almaden have demonstrated that the TrueNorth brain-inspired computer chip, with its 1 million neurons and 256 million synapses, can efficiently implement inference with deep networks that approach state-of-the-art classification accuracy on several vision and speech datasets. This will open up the possibilities of embedding intelligence in the entire computing stack […]

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Unsupervised learning with artificial neurons

Inspired by the way the human brain functions, a team of scientists at IBM Research in Zurich, have imitated the way neurons spike, for example when we touch a hot plate. These so-called artificial neurons can be used to detect patterns and discover correlations in Big Data with power budgets and at densities comparable to […]

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