Neural Processing Engine

Overview

Roviero 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.

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.

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

Benefits

  • 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
  • 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

Technical Specifications

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Semiconductor IP