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Neural Network Engine IP

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Compare 43 IP from 18 vendors (1 - 10)
  • Compact neural network engine offering scalable performance (32, 64, or 128 MACs) at very low energy footprints
    • Best-in-Class Energy
    • Enables Compelling Use Cases and Advanced Concurrency
    • Scalable IP for Various Workloads
    Block Diagram -- Compact neural network engine offering scalable performance (32, 64, or 128 MACs) at very low energy footprints
  • AI-Capable 3D GPU
    • The AI-GPU cores come fully packed with cutting-edge features accessible through the latest industry standard APIs, including Vulkan®, OpenGL® ES, OpenVX™, OpenCL®, DirectX, and OpenVG™
    • It also supports the latest AI and ML frameworks and APIs, including TensorFlow, TensorFlow Lite, Caffe, Caffe2, Darknet, Android NN, ONNX.
    • Building on success across a wide range of market segments, Vivante support for operating systems now includes the latest Android, ChromeOS, GoogleTV, Linux, Windows, QNX, Green Hills, and other ISV platforms.
  • AI-Capable 3D GPU
    • The AI-GPU cores come fully packed with cutting-edge features accessible through the latest industry standard APIs, including Vulkan®, OpenGL® ES, OpenVX™, OpenCL®, DirectX, and OpenVG™ It also supports the latest AI and ML frameworks and APIs, including TensorFlow, TensorFlow Lite, Caffe, Caffe2, Darknet, Android NN, ONNX. Building on success across a wide range of market segments, Vivante support for operating systems now includes the latest Android, ChromeOS, GoogleTV, Linux, Windows, QNX, Green Hills, and other ISV platforms.
  • AI-Capable 3D GPU
    • The AI-GPU cores come fully packed with cutting-edge features accessible through the latest industry standard APIs, including Vulkan®, OpenGL® ES, OpenVX™, OpenCL®, DirectX, and OpenVG™ It also supports the latest AI and ML frameworks and APIs, including TensorFlow, TensorFlow Lite, Caffe, Caffe2, Darknet, Android NN, ONNX. Building on success across a wide range of market segments, Vivante support for operating systems now includes the latest Android, ChromeOS, GoogleTV, Linux, Windows, QNX, Green Hills, and other ISV platforms.
  • DPU for Convolutional Neural Network
    • Configurable hardware architecture
    • Configurable core number up to three
    • Convolution and deconvolution
  • Complete Neural Processor for Edge AI
    • Designed for Low-Power Neural Network Processing
    • Flexible Training Methods
    • Scalable Neuron Fabric
    Block Diagram -- Complete Neural Processor for Edge AI
  • Fusion Recurrent Neural Network (RNN) Accelerator
    • MAC utilization up to 99%
    • Energy efficiency 2.06 TOPS/W
    • Peak performance can scale up to 204.8 GOPS
    Block Diagram -- Fusion Recurrent Neural Network (RNN) Accelerator
  • ARC NPX Neural Processing Unit (NPU) IP supports the latest, most complex neural network models and addresses demands for real-time compute with ultra-low power consumption for AI applications
    • ARC processor cores are optimized to deliver the best performance/power/area (PPA) efficiency in the industry for embedded SoCs. Designed from the start for power-sensitive embedded applications, ARC processors implement a Harvard architecture for higher performance through simultaneous instruction and data memory access, and a high-speed scalar pipeline for maximum power efficiency. The 32-bit RISC engine offers a mixed 16-bit/32-bit instruction set for greater code density in embedded systems.
    • ARC's high degree of configurability and instruction set architecture (ISA) extensibility contribute to its best-in-class PPA efficiency. Designers have the ability to add or omit hardware features to optimize the core's PPA for their target application - no wasted gates. ARC users also have the ability to add their own custom instructions and hardware accelerators to the core, as well as tightly couple memory and peripherals, enabling dramatic improvements in performance and power-efficiency at both the processor and system levels.
    • Complete and proven commercial and open source tool chains, optimized for ARC processors, give SoC designers the development environment they need to efficiently develop ARC-based systems that meet all of their PPA targets.
  • AI processing engine
    • Voice control, Context detection and Sensor applications
    • High Accuracy in Noisy Conditions
  • AI processing engine
    • Voice control, Context detection and Speaker ID
    • High Accuracy in Noisy Conditions
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