Edge AI Processor IP

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Compare 35 IP from 25 vendors (1 - 10)
  • AI Processor Accelerator
    • Universal Compatibility: Supports any framework, neural network, and backbone.
    • Large Input Frame Handling: Accommodates large input frames without downsizing.
    Block Diagram -- AI Processor Accelerator
  • 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
  • High-performance 32-bit multi-core processor with AI acceleration engine
    • Instruction set: T-Head ISA (32-bit/16-bit variable-length instruction set);
    • Multi-core: Isomorphic multi-core, with 1 to 4 optional cores;
    • Pipeline: 12-stage;
    • Microarchitecture: Tri-issue, deep out-of-order;
    Block Diagram -- High-performance 32-bit multi-core processor with AI acceleration engine
  • AI inference processor IP
    • High Performance, Low Power Consumption, Small Foot Print IP for Deep Learning inference processing.
    Block Diagram -- AI inference processor IP
  • Performance AI Accelerator for Edge Computing
    • Up to 16 TOPS
    • Up to 16 MB Local Memory
    • RISC-V/Arm Cortex-R or A 32-bit CPU
    • 3 x AXI4, 128b (Host, CPU & Data)
    Block Diagram -- Performance AI Accelerator for Edge Computing
  • Neural engine IP - The Cutting Edge in On-Device AI
    • The Origin E6 is a versatile NPU that is customized to match the needs of next-generation smartphones, automobiles, AV/VR, and consumer devices.
    • With support for video, audio, and text-based AI networks, including standard, custom, and proprietary networks, the E6 is the ideal hardware/software co-designed platform for chip architects and AI developers.
    • It offers broad native support for current and emerging AI models, and achieves ultra-efficient workload scheduling and memory management, with up to 90% processor utilization—avoiding dark silicon waste.
    Block Diagram -- Neural engine IP - The Cutting Edge in On-Device AI
  • Neural network processor designed for edge devices
    • High energy efficiency
    • Support mainstream deep learning frameworks
    • Low power consumption
    • An integrated AI solution
  • Scalable Edge NPU IP for Generative AI
    • Ceva-NeuPro-M is a scalable NPU architecture, ideal for transformers, Vision Transformers (ViT), and generative AI applications, with an exceptional power efficiency of up to 3500 Tokens-per-Second/Watt for a Llama 2 and 3.2 models
    • The Ceva-NeuPro-M Neural Processing Unit (NPU) IP family delivers exceptional energy efficiency tailored for edge computing while offering scalable performance to handle AI models with over a billion parameters.
    Block Diagram -- Scalable Edge NPU IP for Generative AI
  • Multi-core capable RISC-V processor with vector extensions
    • The SiFive® Intelligence™ X280 Gen 2 is an 8-stage dual issue, in-order, superscalar design with wide vector processing (512 bit VLEN/256-bit DLEN).
    • It supports RISC-V Vectors v1.0 (RVV 1.0) and SiFive Intelligence Extensions to accelerate critical AI/ML operations.
    Block Diagram -- Multi-core capable RISC-V processor with vector extensions
  • Custom RISC-V Processor
    • Traditional processors no longer strike the right balance between high performance, energy consumption, and cost.
    • Keysom processors deliver powerful capabilities, optimizing IoT and AI workflows with energy-efficient, small-footprint solutions.
    Block Diagram -- Custom RISC-V Processor
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Semiconductor IP