Solution

Processors for EDGE computing suffer from:


  • Having limited processing speed
  • Consuming too much power
  • Poor utilization of silicon
  • Running high clock cycles

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    Feeding the 3-dimensional neural network data using a traditional instruction set makes the compiler intangible in terms of achieving utilization and there by low power and size.



    Gyrus AI has developed a natively graph computing processor for edge inference. CortiCore architecture provides the solution via its unique instruction set that dramatically reduces the compiler complexity.


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    The approach allows us to create a compiler that achieves >80% utilization with 16X reduced memory* on all neural networks – demonstrated on our FPGA platforms.

    *compared to currently available solutions

    You no longer have to settle for low power vs performance!

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    Check our demo system.

    Key features

    Internal Memory

  • Low internal memory requirement (min 256KB)
  • flexible tradeoff on performance and memory

    • External memory

  • Sleeps > 99% of the time
  • Low power: access one time per input frame

    • High Utilization

  • > 80% utilization for all types of model structures
  • Efficiently handle weight-stationary & Datastationar

    • Power Consumption

  • Achieves micro-Watt power when incumbents struggle with milli-Watts

    • Speed

  • Scalable from 0.1TOPS to 100TOPS
  • Runs at low clock-cycle- 10-30x better
  • Compiler designed to bring up networks efficiently
  • Support large input frame without down scaling

    • Configuration

  • Flexibility to reconfiqure/extend to support current and future application models

    • The magic in Gyrus AI's NPE (Neural Processing Engine) happens in the software domain by what we call CortiSoft. Our compilers and software tools allow for the porting of any neural network to run on CortiOne hardware accelerator enabling a highly efficent compute of large data.

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    Scalability

  • Scalable RTL via parameters for performance and power
  • Number of ALU
  • Number of clusters
  • Activation memory size per cluster
  • DDR or No DDR – external memory
  • Internal system memory
  • External shared memory
  • Hardware confguration input to compiler



    • Additional Key Features

  • Any frameworks, any NN, any backbone

  • AI optimized instruction set – makes compiler possible

  • AI Data movement and compute-oriented instructions

  • >80% compute utilization

  • Highly parallel design – high performance at low frequency of operation

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  • Implements sparse NN efficiently, reducing model size and compute requirement by >3x

  • All digital logic – implement in any process node

  • Very low host code support to run the AI processing job