軟件優先的方法 ai在邊緣.pdf

1、Tim MamtoraChief of Innovation&Engineering,ImaginationSpeaker:A software-first approach to Huge societal opportunity for AI to“do good”Source:IDC,#US50888824,May 2024Worldwide Edge Endpoint AI Processor and Accelerator Forecast,20242028Challenges at the edge are all too familiarThe biggest lesson th

2、at can be read from 70 years of AI research is that general methods that leverage computation are ultimately the most effective,and by a large margin”Two key lessons:Scalable and OpenDirectly competing with open source is a losing propositionOpen-source models are faster,more customizable,more priva

3、te and pound-for-pound more capable”Hotspot management challengesSilicon integration challenges with increasing transistor count per deviceIP selection and supply challenges beyond 2nmHeterogenous programming challengesSimpler thermal managementMore tractable silicon integration whether monolithic,2

4、D or 3DSimpler IP management scenarioIt scales!Its simple to use!Traditional Edge Device Model Domain Specific Accelerators(DSAs)and rich IP ecosystemFuture Edge Device Model Borrowing from cloud design philosophyTaking a hardware-first viewTaking a software-first viewNon-proprietary entry pointsAvo

5、idance of premature optimisationAvoidance of premature optimisationGood quality and extensive libraries and tools User can exert fine control over the hardwareSufficient degrees of freedom to address workload specific needsComputational Generality and Ecosystem CompatibilityEase-of-use and Good SW a

6、nd LibrariesSolution-level Energy EfficiencyEnter the Unified Acceleration Foundation(UXL)Build a multi-architecture,multi-vendor software ecosystem for all acceleratorsUnify the heterogeneous compute ecosystem around open standardsBuild on and expand open-source projects for accelerated computingSt

7、eering MembersContributor MembersAffiliationsA fully supported developer journeyLibraries,models,toolkits and profilers are the productLeverage open-source for speed/flexibilityShift left with performance-indicative modelsGuide workload deployment through reference stacksFlexible architectures for d

8、ynamic operationEdge autonomy moves us on even further and emphasises the importance of a software-first mindset Functional safety/SILConstitutional AI/XAIOpen,modifiable software On-device tuningImagination enables success at the edgeConclusions:Take a software-first approachEdge opportunity“for good”is enormousDefragmentation essential through open software collaborationSuccess at the edge requires a software-first mindsetAutonomy increases need for flexibility and transparencyHardware innovation continues abound