i am not sure about this if it was posted before ? SNAP Core -...

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SNAP Core - Spiking Neuron Adaptive Processor Core

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Spiking neural networks implemented with SNAP core operate in parallel, making them significantly faster than software neural networks running on Central Processing Units (CPUs) or Graphics Processing Units (GPUs). SNAP is more energy efficient, enabling SNN to be integrated into portable devices for local processing of sensor data. SNAP based neural networks can respond in real time with low latency, regardless of the neural network size. SNAP implements learning rules in hardware, enabling Autonomous Features Extraction (AFE) directly from input data without need for any software processing .

SNAP based neuromorphic chips can be used in nearly any embedded systems that requires pattern recognition or AFE locally without having to go to cloud based computations. Such systems represent a massive and unlimited potential market, with applications in Smartphones, Internet of Things (IoT), machine to machine (M2M), robotics, gaming, driverless vehicles, drones and air transportation among others.

Deliverables

• SNAP based Spiking Neural Network RTL
• Configurable Neuron and Synapses
• Configurable Learning rules
• Configurable synaptic connectivity
• Proprietary spike communication protocol
• Direct connection to sensory inputs
• Creation of libraries of learned behaviors
• SNAP Models Integrated with Neural Network Simulators like NENGO*
• Design services for SNAP customization
• Hardware emulation in FPGA
• Configuration management tools

Tech Specs

	Column 1	Column 2
0	Part Number	SNAP Core
1	Short description	Spiking Neuron Adaptive Processor Core
2	Provider:	T2M
3	Compliant Standard	Yes
4	Maturity	In Production
5	Availability	Immediate

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• T2M Silicon IP Portfolio
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Updated: Aug. 08, 2016 ​

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Features

• Neural Network updates at 1MHz
• Fully parallel computation
• Real time pattern recognition
• Input from DVS camera
• Unsupervised feature learning
• Spike Timing Dependent Plasticity
• Library of learned features
• Labeled output (vehicles counts)

Benefits

• Fast, independent of size
• Highly energy efficient
• Real time response
• UnsupervisedLearning
• Actionable data
• CustomizableNetwork

Applications

• Surveillance and security cameras
• Smartphones
• Internet of Things (IoT)
• Machine to machine (M2M)
• Robotics, Gaming
• Driverless Vehicles
• Drones and Air Transportation

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