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CNN IP

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Compare 45 IP from 18 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.
    Block Diagram -- AI Accelerator Specifically for CNN
  • 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)
    Block Diagram -- Convolutional Neural Network (CNN) Compact Accelerator
  • Verification IP for CSI/DSI/C-PHY/D-PHY
    • A comprehensive VIP solution for CSI-2, DSI-2, D-PHY and C-PHY transmitter and receiver designs.
    • CSI/DSI-Xactor implements a complete set of models, protocol checkers and compliance testsuites in 100% native SystemVerilog and UVM.
  • NPU IP Core for Edge
    • Origin Evolution™ for Edge offers out-of-the-box compatibility with today's most popular LLM and CNN networks. Attention-based processing optimization and advanced memory management ensure optimal AI performance across a variety of networks and representations.
    • Featuring a hardware and software co-designed architecture, Origin Evolution for Edge scales to 32 TFLOPS in a single core to address the most advanced edge inference needs.
    Block Diagram -- NPU IP Core for Edge
  • NPU IP Core for Mobile
    • Origin Evolution™ for Mobile offers out-of-the-box compatibility with popular LLM and CNN networks. Attention-based processing optimization and advanced memory management ensure optimal AI performance across a variety of today’s standard and emerging neural networks.
    • Featuring a hardware and software co-designed architecture, Origin Evolution for Mobile scales to 64 TFLOPS in a single core.
    Block Diagram -- NPU IP Core for Mobile
  • NPU IP Core for Data Center
    • Origin Evolution™ for Data Center offers out-of-the-box compatibility with popular LLM and CNN networks. Attention-based processing optimization and advanced memory management ensure optimal AI performance across a variety of today’s standard and emerging neural networks. Featuring a hardware and software co-designed architecture, Origin Evolution for Data Center scales to 128 TFLOPS in a single core, with multi-core performance to PetaFLOPs.
    Block Diagram -- NPU IP Core for Data Center
  • NPU IP Core for Automotive
    • Origin Evolution™ for Automotive offers out-of-the-box compatibility with popular LLM and CNN networks. Attention-based processing optimization and advanced memory management ensure optimal AI performance across a variety of today’s standard and emerging neural networks.
    • Featuring a hardware and software co-designed architecture, Origin Evolution for Automotive scales to 96 TFLOPS in a single core, with multi-core performance to PetaFLOPs.
    Block Diagram -- NPU IP Core for Automotive
  • 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.
    Block Diagram -- IP library for the acceleration of edge AI/ML
  • Specialized Video Processing NPU IP
    • Highly optimized for CNN-based image processing application
    • Fully programmable processing core: Instruction level coding with Chips&Media proprietary Instruction Set Architecture (ISA)
    • 16-bit floating point arithmetic unit
    • Minimum bandwidth consumption
    Block Diagram -- Specialized Video Processing NPU IP
  • Highly scalable performance for classic and generative on-device and edge AI solutions
    • Flexible System Integration: The Neo NPUs can be integrated with any host processor to offload the AI portions of the application
    • Scalable Design and Configurability: The Neo NPUs support up to 80 TOPS with a single-core and are architected to enable multi-core solutions of 100s of TOPS
    • Efficient in Mapping State-of-the-Art AI/ML Workloads: Best-in-class performance for inferences per second with low latency and high throughput, optimized for achieving high performance within a low-energy profile for classic and generative AI
    • Industry-Leading Performance and Power Efficiency: High Inferences per second per area (IPS/mm2 and per power (IPS/W)
    Block Diagram -- Highly scalable performance for classic and generative on-device and edge AI solutions
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