[{"data":1,"prerenderedAt":-1},["ShallowReactive",2],{"branding":3,"analytics":7,"article-a-smarter-way-to-split-ml-work-between-cpus-and-memory-chips":10,"sections":34},{"siteName":4,"siteTagline":5,"publisherName":4,"contactEmail":6},"The Revision","Tech news, decoded.","editor@therevision.news",{"gaMeasurementId":8,"adsenseClientId":9},"G-ZW2MV82GYR","ca-pub-8533917693782264",{"article":11},{"id":12,"slug":13,"title":14,"dek":15,"body_md":16,"tags_json":17,"published_at":18,"created_at":19,"updated_at":20,"status":21,"review_note":22,"review_notes":23,"image_url":22,"persona_id":22,"persona_name":22,"section":24,"tags":25,"sources":29,"feedback":33,"feedback_at":22,"cost_usd":33,"total_tokens":33},4020,"a-smarter-way-to-split-ml-work-between-cpus-and-memory-chips","A Smarter Way to Split ML Work Between CPUs and Memory Chips","Researchers propose an ILP-based framework that cuts inference latency by routing ML tasks across CPUs and CIM accelerators — up to 30.9x faster than CPU-only.","A new partitioning framework promises to make computing-in-memory hardware far more useful for running ML models at the edge.\n\nResearchers published a method for splitting machine learning workloads between traditional CPUs and Computing-in-Memory accelerators — chips that do math directly inside memory rather than shuttling data to a separate processor. The paper targets resistive RAM-based CIM hardware, which comes with real constraints: limited storage, slow write speeds, and a finite number of write cycles before the memory degrades. Prior partitioning approaches largely ignored those constraints, and ignored the CPU as a useful co-processor. The new framework uses Integer Linear Programming to find the optimal split, factoring in parallelism and low-level hardware behavior alongside those RRAM limits.\n\nThe numbers are notable: heterogeneous CPU-CIM execution hit up to 30.9x faster inference than running on an edge CPU alone, and 7.3x faster than a high-performance desktop CPU. That gap matters because edge inference — running models locally on devices rather than in the cloud — is where power budgets and memory constraints bite hardest. A framework that wrings more speed from constrained hardware without ignoring its failure modes is the kind of unglamorous work that actually ships.\n\nCIM accelerators have been a research darling for years, but the gap between lab results and deployable systems has stayed stubbornly wide — largely because papers optimize for peak throughput and gloss over endurance or write-latency penalties. Whether this framework closes that gap in production silicon remains to be seen.","[\"machine learning\",\"hardware\",\"edge computing\",\"ai\"]","2026-07-07T04:00:00.000Z","2026-07-07T15:08:58.417Z","2026-07-07T15:09:01.327Z","published",null,[],"ai",[26,27,28,24],"machine learning","hardware","edge computing",[30],{"name":31,"url":32},"arXiv cs.AI","https:\u002F\u002Farxiv.org\u002Fabs\u002F2607.05240",0,{"sections":35},[36,40,45,50,55,59,64,69,74,78,83,87,92,97],{"name":37,"slug":24,"count":38,"latest_published_at":39},"AI",2590,"2026-07-16T04:00:00.000Z",{"name":41,"slug":42,"count":43,"latest_published_at":44},"Security","security",294,"2026-07-15T19:59:48.000Z",{"name":46,"slug":47,"count":48,"latest_published_at":49},"Deals","deals",179,"2026-06-29T20:02:07.000Z",{"name":51,"slug":52,"count":53,"latest_published_at":54},"Policy","policy",158,"2026-07-16T00:02:48.000Z",{"name":56,"slug":27,"count":57,"latest_published_at":58},"Hardware",122,"2026-07-14T19:46:26.000Z",{"name":60,"slug":61,"count":62,"latest_published_at":63},"Consumer Tech","consumer-tech",93,"2026-07-13T13:20:48.000Z",{"name":65,"slug":66,"count":67,"latest_published_at":68},"Software","software",70,"2026-07-13T19:52:25.000Z",{"name":70,"slug":71,"count":72,"latest_published_at":73},"Science","science",66,"2026-07-10T10:29:37.000Z",{"name":75,"slug":76,"count":77,"latest_published_at":18},"Dev Tools","dev-tools",59,{"name":79,"slug":80,"count":81,"latest_published_at":82},"Gaming","gaming",41,"2026-07-09T04:00:00.000Z",{"name":84,"slug":85,"count":81,"latest_published_at":86},"Startups","startups","2026-06-29T20:55:50.000Z",{"name":88,"slug":89,"count":90,"latest_published_at":91},"General","general",29,"2026-07-10T22:28:58.000Z",{"name":93,"slug":94,"count":95,"latest_published_at":96},"Reviews","reviews",20,"2026-06-24T12:00:01.000Z",{"name":98,"slug":99,"count":100,"latest_published_at":101},"How-To","how-to",6,"2026-06-16T09:00:00.000Z"]