CNN IP

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Compare 34 IP from 13 vendors (1 - 10)

AI Accelerator Specifically for CNN
- A specialized hardware with controlled throughput and hardware cost/resources, utilizing parameterizeable layers, configurable weights, and precision settings to support fixed-point operations.
- This hardware aim to accelerate inference operations, particulary for CNNs such as LeNet-5, VGG-16, VGG-19, AlexNet, ResNet-50, etc.
Convolutional Neural Network (CNN) Compact Accelerator
- Support convolution layer, max pooling layer, batch normalization layer and full connect layer
- Configurable bit width of weight (16 bit, 1 bit)
High performance-efficient deep learning accelerator for edge and end-point inference
- Configurable MACs from 32 to 4096 (INT8)
- Maximum performance 8 TOPS at 1GHz
- Configurable local memory: 16KB to 4MB
Neuromorphic Processor
- Neural Processing Unit (NPU) at memory compute architecture implementing Integrate and fire neuron.
- Emulate multiple neurons with configurable Synapses.
- Compute only when events occur.
- Up-to 4 bits for weights and activation.
IP cores for ultra-low power AI-enabled devices
- Ultra-fast Response Time
- Zero-latency Switching
- Low Power
Sensor Fusion IP
- Kalman Filter
- Extended Kalman Filter
- CNN
Machine Learning Processor
- Partner Configurable
- Extremely Small Area
- Single Toolchain
Neural network processor designed for edge devices
- High energy efficiency
- Support mainstream deep learning frameworks
- Low power consumption
- An integrated AI solution
Accelerator for Convolutional Neural Networks
- Include VGG, ResNet, MobileNet, and other custom use cases.
AI Accelerator
- Independent of external controller
- Accelerates high dimensional tensors
- Highly parallel with multi-tasking or multiple data sources
- Optimized for performance / power / area