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xcore.ai is a fast, flexible and economical
crossover processor for the AIoT

HIGH SPEED

LOW ENERGY

FAST INFERENCING

HIGH FLEXIBILITY

LOW COST

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Introducing a fast, flexible and economical platform for the AIoT, which delivers high performance, AI, DSP, IO and control in a single device.

xcore.ai is the new crossover processor that delivers on flexibility without sacrificing performance.

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xcore.ai enables developers to embed intelligence at the edge. With fully programmable ports, fast processing and neural network capabilities, xcore.ai processes data locally and takes actions on device – within nanoseconds. With highly efficient computation of deep neural networks, inferences and characterisation of 8bit and binarized network models are supported (as well as 16bit and 32bit). xcore.ai is programmable in C using industry standard tools, and developers can access machine learning capabilities and fine tune their models for accuracy and performance using standard frameworks such as TensorFlow Lite.

On-device intelligence

xcore.ai is architected to deliver tailored, functional performance for AIoT applications. With 16 logical cores, the new crossover processor provides efficient, high performance compute at the edge, enabling sensing, signal processing, machine learning, control and communication to all be processed concurrently. Unlike other processors, xcore.ai is lightning fast and predictable, with execution determinism measured in single cycles – which means it takes milliseconds to boot from zero-power and nanoseconds to transition from low power standby to full performance.

Compute

Utilising the four types of compute on xcore.ai, manufacturers can design new intelligent product types for the AIoT, as well as enhancing electronics that already surround us. xcore.ai has the highest processing power and most flexibility for the cost. This means that electronics manufacturers (no matter their size) can embed more intelligence in IoT devices to make life simpler, safer and more satisfying for all.

Flexibility

Cost is a significant consideration for any designer of smart products. Today’s devices require either multiple discrete components or bigger, more expensive processors or SoCs to deliver sufficient performance, inferencing and connectivity. With flexible forms of compute and connectivity in a single device, xcore.ai keeps the eBOM costs low, and design potential high.

Cost

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32 x
AI performance

16 x
faster I/O processing

15 x
DSP performance

21 x
more 16-bit MACs

Ultra AI performance


Ultra fast processing


Ultra energy efficient


Ultra low cost


xcore.ai

ARM Cortex M7 @ 600 MHz

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smart home
Example applications:
Smart speaker
Audio visual
Appliances
Lighting
Security

smart home

connected health
Example applications:
Fitness
Care
Diagnostics & monitoring
MHealth

connected health

industrial iot
Example applications:
Operations
Tracking
Safety
Maintenance
Energy management

industrial IoT

smart cities
Example applications:
Traffic & parking
Environmental
Utilities
Public safety & security

smart city

smart mobility
Example applications:
Asset tracking & predictive maintenance
In-car people tracking
Autonomous L1 driving & safety

smart mobility

example use cases

testimonials

“The more I think about it, the more I can envisage these xcore.ai devices being deployed in almost everything.”

Max Maxfield, eeJournal

“Significant AI capabilities further sets xcore.ai apart as a flexible and integrated solution for Industrial IoT.”

Nikolai Ensslen, President & CEO, Synapticon

“Using XMOS we were able to replace three separate development systems, saving us over 6 months in development time.  All our new products now use xcore”

Meridian

xcore.ai in the press

For all enquiries, please contact press@xmos.com

XMOS adapts xcore into AIoT ‘crossover processor’
Coverage

XMOS adapts xcore into AIoT ‘crossover processor’

11th February 2020
Read this exclusive from EE Times, announcing xcore.ai – our new crossover processor for the AIoT.

xcore.ai on the road

16th – 16th June
Las Vegas, NV

22nd – 24th June
San Jose, CA

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