CNN Accelerator IP

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Compare 7 IP from 7 vendors (1 - 7)

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)
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.
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
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
Sensor Fusion IP
- Kalman Filter
- Extended Kalman Filter
- CNN
IP library for the acceleration of edge AI/ML
- A library with a wide selection of hardware IPs for the design of modular and flexible SoCs that enable end-to-end inference on miniaturized systems.
- Available IP categories include ML accelerators, dedicated memory systems, the RISC-V based 32-bit processor core icyflex-V, and peripherals.