Overview

Faster Product Development for the Evolving AI Market

Developing an agile software stack is important for successful artificial intelligence and machine learning (AI/ML) deployment at the edge and on device. The Cadence NeuroWeave Software Development Kit (SDK) provides customers with a uniform, scalable, and configurable ML interface and tooling that significantly improves time to market and better prepares them for a continuously evolving AI market.

As new ML models are created from multiple AI frameworks that leverage the latest primitives and state-of-the-art topologies, running these models efficiently on a high-performance, up-to-date AI software stack can be a complex undertaking. With the proliferation of AI inferencing across multiple market segments, SoC designers are working harder to meet product development timelines and yet provide differentiation across their product portfolio.

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Key Benefits

Leveraging Automated Toolchain to Reduce Time to Market

Pre-Silicon Simulation Environment for Evaluating AI Workloads for Vision, Audio, NLP, Robotics, Etc.

Turnkey software to enable AI inferencing for edge and on-device applications

Provide Key KPIs for AI Inferencing Deployments

Extract all the meaningful metrics for AI inferencing using cycle accurate analysis software and simulation platforms

Hardware/Software Co-Design Enablement

Enables both hardware and software tuning to deliver the best PPA to meet application-specific design goals

Features

Key Features of an Agile AI Compiler Stack

The NeuroWeave SDK is a high-performance machine-learning compiler stack offering various techniques such as uniform front-end design to lossless compression, which will help customers with various AI deployment scenarios.

  • Front-End:
    Open-source-based front-end model import and common IP across all Cadence IP targets
  • Complier Infrastructure:
    A reusable, extensible complier that leverages next-gen open-source ML complier frameworks
  • Workload Mapping:
    Extract best performance on hardware by using mixed-precision support quantization flow and numerous graph optimizations to get best in-class PPA
  • Heterogenous Compute:
    Simple offload APIs for ease of software integration in a complex heterogenous SoC
  • Quick Deployment:
    Tools for end-to-end debugging, extracting key metrics for reporting and logging

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