LONDON, July 18, 2017 /PRNewswire/ — Deep learning technology is driving the evolution of artificial intelligence (AI) and has become one of the hottest topics of discussion within the technology world and beyond. Given the rate at which deep learning is progressing, some industry observers are predicting it will bring about a doomsday scenario, while others strive for a time when the technology can transform business processes and create new business models through scalable, efficient automation and predictive capabilities.

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Against the backdrop of rapid technological development, the current market climate is ripe for innovation in hardware in general, and chipsets more specifically. The chipset market today is led by graphics processing units (GPUs) and central processing units (CPUs), but during the coming years there will be an expanded role for other chipset types including application specific integrated circuits (ASICs), field programmable gate arrays (FPGAs), and other emerging chipsets.

Deep learning technology has primarily been a software play so far. The need for hardware acceleration was recognized in academia, but few companies ventured into developing specialized chipsets until now. AI applications demand higher performance and lower power. The world’s top semiconductor companies as well as a number of startups have jumped into the race to meet these demands and this report attempts to capture state-of-the-art offerings currently available in the
industry. Tractica forecasts that the deep learning chipset market revenue will increase from $513 million in 2016 to $12.2 billion by 2025 at a compound annual growth rate (CAGR) of 42.2%.

This Tractica report discusses deep learning chipsets from the points of view of both the market and the technology. Tractica segments the application markets, as well as the chipsets themselves by category, and provides market forecasts for the period from 2016 through 2025. Deep learning technology as it pertains to chipsets is very technical and Tractica explains the various approaches different vendors are taking to solve the problem and the tradeoffs involved. A comprehensive listing
of the companies in this market is also provided.

Key Market Forecasts
– Deep Learning Chipset Unit Shipments and Revenue
by Type, World Markets: 2016-2025
– Deep Learning Chipset Revenue, Inference versus
Training, World Markets: 2016-2025
– Deep Learning Chipset Revenue by Compute
Capacity, World Markets: 2016-2025
– Deep Learning Chipset Revenue by Power
Consumption, World Markets: 2016-2025
– Deep Learning Chipset Average Selling Price by Type,
World Markets: 2016-2025
– Deep Learning Chipset Revenue by Market Sector,
World Markets: 2016-2025
– Deep Learning CPU Unit Shipments and Revenue,
World Markets: 2016-2025
– Deep Learning GPU Unit Shipments and Revenue,
World Markets: 2016-2025
– Deep Learning ASIC Unit Shipments and Revenue,
World Markets: 2016-2025
– Deep Learning FPGA Unit Shipments and Revenue,
World Markets: 2016-2025
– Deep Learning Other Chipset Unit Shipments and
Revenue, World Markets: 2016-2025

Chipset Types
– CPU
– GPU
– FPGA
– ASIC
– Other

Power Consumption
– High (>100 W)
– Medium (5-100 W)
– Low (

<5 W)>Compute Capacity
– High (> 1TFlops)
– Low (< 1TFlops)

Application Domains
– Training
– Inference
– Inference & Training

Market Sectors
– Enterprise
– Consumer
– Defense

Geographies
– North America
– Europe
– Asia Pacific
– Latin America
– Middle East and Africa
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