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Habana Labs Announces Best-Performing AI Inference Processor 

Habana Labs Announces Best-Performing AI Inference Processor  | Language Tech Market News | Scoop.it

Habana Labs, Ltd. (www.habana.ai), is sampling its first AI processor to select customers. A PCIe card based on its Goya HL-1000 processor delivers 15,000 images/second throughput on the ResNet-50 inference benchmark, with 1.3 milliseconds latency, while consuming only 100 watts of power.  Habana Labs' AI processors offer one to three orders of magnitude better performance than solutions commonly deployed in data centers today.

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TensorRT 4 Accelerates Neural Machine Translation, Recommenders, and Speech

TensorRT 4 Accelerates Neural Machine Translation, Recommenders, and Speech | Language Tech Market News | Scoop.it
NVIDIA has released TensorRT 4 at CVPR 2018. This new version of TensorRT provides new RNN layers, new Multilayer perceptron (MLP) operations, and optimizations for Recommender Systems. Native ONNX parser to import models from popular deep learning frameworks
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Deep Learning Chipset Market to Grow at a CAGR +52% by 2022

Deep Learning Chipset Market to Grow at a CAGR +52% by 2022 | Language Tech Market News | Scoop.it

Players here include NVIDIA, Intel, IBM, Qualcomm, CEVA, KnuEdge, AMD, Xilinx, ARM, Google, Graphcore, TeraDeep, Wave Computing, BrainChip.

LT-Innovate's insight:

Even more powerful but cheaper chips will pave the way to ambient intelligence, (VR, AR, machine learning in your browser etc.

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@Qualcomm Chips to Take on Speech Translation

@Qualcomm Chips to Take on Speech Translation | Language Tech Market News | Scoop.it

The next step for audio technology is instant translation. “Instant translation means eliminating the barriers of language,” Chris said, “so you can readily have a conversation with someone without knowing their language, which would particularly help those who do business for instance in China. There are a huge breadth of challenges and developments which we are keen to address at Qualcomm.”

LT-Innovate's insight:

First, the new chips will simply improve sound quality and processing speeds. But translation in a chip sounds about right for the usual "5 years' time" horizon

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The Promise for Cognitive Computing of Low-Power Super-fast Microchips

The Promise for Cognitive Computing of Low-Power Super-fast Microchips | Language Tech Market News | Scoop.it

A team led by researchers from Exeter, Oxford, and Münster universities has developed specialized photonic microchips, which could one day help to create computers that can store and process huge amounts of information at faster speeds than the human brain can. Because the microchips are powered by light, they could also execute high-speed computing at a lower power supply than could ever be possible with any electronic processing system.

LT-Innovate's insight:

The tech industry needs to keep an eye on compute hardware transforming data management and machine learning among other things. Low-power/high speed chips will directly drive opportunities for language technologies among others.

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Deep Learning Chip Market Growing at ~40% CAGR

Deep Learning Chip Market Growing at ~40% CAGR | Language Tech Market News | Scoop.it

According to a report by Allied Market Research, (Global Deep Learning Chip Market by Chip type, Technology, and Industry vertical: Global Opportunity Analysis and Industry Forecast, 2018-2025), the global deep learning chip market was valued at $1,975 million in 2017, and is projected to reach $29,368.0 million by 2025, growing at a CAGR of 39.9% from 2018 to 2025. At present, North America dominates the market, followed by Europe. In 2017, U.S. dominated the North America market and UK led the overall market in Europe. However, in the Asia-Pacific region, China currently dominates the market.

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Big Bets on A.I. Open a New Frontier for Chip Start-Ups

Big Bets on A.I. Open a New Frontier for Chip Start-Ups | Language Tech Market News | Scoop.it

Today, at least 45 start-ups are working on chips that can power tasks like speech and self-driving cars, and at least five of them have raised more than $100 million from investors. Venture capitalists invested more than $1.5 billion in chip start-ups last year, nearly doubling the investments made two years ago, according to the research firm CB Insights.
The explosion is akin to the sudden proliferation of PC and hard-drive makers in the 1980s. While these are small companies, and not all will survive, they have the power to fuel a period of rapid technological change.

LT-Innovate's insight:

Presumaby more and more cognitive processing will be miniaturized to chips, enabling more and more hearables, wearables, and other very smart equipment to act as intimate prostheses of our communicational skill sets.

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Xiaomi Will Launch Phones With #AI Chips in 2018

Xiaomi Will Launch Phones With #AI Chips in 2018 | Language Tech Market News | Scoop.it

During CEO Lei Jun's speech at the 4th World Internet Conference held in Wuzhen, China, he said that in the next 10 years, the company’s core strategy would focus on Artificial Intelligence. He disclosed that the next generation of Xiami phones expected to be released in 2018 will incorporate Artificial Intelligence. The tech giant is expected to debut with the Xiaomi Mi 7 packing a Snapdragon 845 chip in the early part of next year. 

LT-Innovate's insight:

The inevitable next step, using AI to crunch real time data on a phone? Should rewards lots of NLU and related language operations.

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@Graphcore High IPU Performance Speeds Up NLP & Speech Processing

@Graphcore High IPU Performance Speeds Up NLP & Speech Processing | Language Tech Market News | Scoop.it

Graphcore (UK) : All recurrent networks contain data dependencies that are a challenge for current chip architectures. The data dependencies limit the amount of parallelism available and the number of operations per data fetch from memory is limited. The IPU and Poplar libraries handle these limitations better through the availability of large amounts of on-chip memory and the flexibility of compute and data movement within the IPU.

LT-Innovate's insight:

Major speed boost for machine learning algorithms could impact machine translation and speech apps.

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