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.
Neural Processing Engine
Overview
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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