Deep Vision exits stealth mode and launches its ARA-1 inference processor to enable the creation of new world AI vision applications at the edge. The processors provide the optimal balance of compute, memory, energy efficiency (2W Typical), and ultra-low latency in a compact form factor, making it the definitive choice for endpoints such as cameras, sensors, as well as edge servers where high compute requirements, model flexibility, and energy efficiency is paramount.
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“Today’s complex AI workloads require not only low power but also low latency to deliver real-time intelligence at the edge,” said Ravi Annavajjhala, CEO of Deep Vision. “No more making tradeoffs between performance and efficiency. Developers now have access to higher accuracy outcomes and rich data insights, all on one processor.”
Groundbreaking High-Efficiency Architecture
Deep learning models are growing in complexity, and driving increased compute demand for AI at the Edge. The Deep Vision ARA-1 Processor is based on a patented Polymorphic Dataflow Architecture, capable of handling varied dataflows to minimize on-chip data movement.
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The architecture supports instructions within each of the neural network models, which allows for optimally mapping any dataflow pattern within a deep learning model. Keeping data close to the compute engines minimizes data movement ensuring high inference throughput, low latency, and greater power efficiency. The compiler automatically evaluates multiple data flow patterns for each layer in a neural network and chooses the highest performance and lowest power pattern.
With its simultaneous multi-model processing, The Deep Vision ARA-1 Processor can also effectively run multiple models without a performance penalty, generating results faster and more accurately. With a lower system power consumption than Edge TPU and Movidius MyriadX, Deep Vision ARA-1 processor runs deep learning models such as Resnet-50 at a 6x improved latency than Edge TPU and 4x improved latency than MyriadX..
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