SNAP Core T2M
Overview:
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.
Applications
Surveillance and security cameras
Smartphones
Internet of Things (IoT)
Machine to machine (M2M)
Robotics, Gaming
Driverless Vehicles
Drones and Air Transportation
Deliverables:
- SNAP based Spiking Neural Network RTL
- Configurable Neuron and Synapses, Learning rules and Configurable synaptic connectivity
- Proprietary spike communication protocol
- Direct connection to sensory inputs
- SNAP Models Integrated with Neural Network Simulators like NENGO*
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)
Details:
Category:
IP Catalog :
Digital Core IP :
Processors :
Other
Portability:
ASIC, FPGA
Process Node:
all
Type:
Soft IP
Maturity:
In Production
Market Category:
Automotive Communications Consumer Electronics Data Processing Industrial and Medical Military/Civil Aerospace
(20min delay)