--- title: Analog AI Is Back, But Can It Survive Its Own Noise? description: AI's energy demands are reviving analog computing, which uses physical properties like conductance, Ohm's law, and Kirchhoff's current law to perform... canonical: https://daily.dev/posts/analog-ai-is-back-but-can-it-survive-its-own-noise--ajhaozm0p twitter:card: summary_large_image twitter:site: @dailydotdev og:type: website og:site_name: daily.dev og:title: Analog AI Is Back, But Can It Survive Its Own Noise? | daily.dev og:description: AI's energy demands are reviving analog computing, which uses physical properties like conductance, Ohm's law, and Kirchhoff's current law to perform... og:url: https://daily.dev/posts/analog-ai-is-back-but-can-it-survive-its-own-noise--ajhaozm0p og:image: https://api.daily.dev/og/posts/AJhaozM0P.png og:image:alt: Analog AI Is Back, But Can It Survive Its Own Noise? og:image:width: 1200 og:image:height: 630 og:locale: en --- > ## Documentation Index > Fetch the complete documentation index at: https://daily.dev/llms.txt > Use this file to discover all available pages before exploring further. # Analog AI Is Back, But Can It Survive Its Own Noise? **[Towards Data Science](https://daily.dev/sources/tds)** · 10 min read · 0 upvotes · 0 comments ## Summary AI's energy demands are reviving analog computing, which uses physical properties like conductance, Ohm's law, and Kirchhoff's current law to perform matrix-vector multiplications directly in memory, bypassing the von Neumann bottleneck. The approach promises dramatic energy savings but faces persistent challenges: programming noise, read noise, ADC/DAC conversion costs, and conductance drift over time. Hands-on numpy simulations demonstrate how a single analog layer can introduce over 8% relative error, and how a normally-trained neural network accuracy collapses sharply past a noise threshold. The key mitigation — noise-injection training (hardware-aware training) — is shown to significantly improve robustness without sacrificing clean-hardware accuracy. The commercial landscape includes IBM Research, EnCharge AI, and Mythic, though the technology remains largely pre-commercial and viable mainly for inference rather than training. ## Full article daily.dev links to this article rather than hosting it. Read it at the original source: ## Similar posts on daily.dev - [Analog computing from waste heat](https://daily.dev/posts/analog-computing-from-waste-heat-awtuq3bap) · MIT Technology Review · 0 upvotes · 0 comments - [The brain power behind sustainable AI](https://daily.dev/posts/the-brain-power-behind-sustainable-ai-xmisw6aog) · MIT News · 0 upvotes · 0 comments - [Researchers Propose Thermodynamic Computing Architecture That Could Dramatically Reduce AI Energy Use](https://daily.dev/posts/researchers-propose-thermodynamic-computing-architecture-that-could-dramatically-reduce-ai-energy-us-yrq4oxapf) · The Quantum Insider · 1 upvotes · 0 comments - [AI Model Creates Conlangs Beyond Human Imagining](https://daily.dev/posts/ai-model-creates-conlangs-beyond-human-imagining-5brkf4fop) · IEEE Spectrum · 0 upvotes · 0 comments - [Overcoming the Twin Traps of AI](https://daily.dev/posts/overcoming-the-twin-traps-of-ai-sxcsxrcnj) · SD Times · 2 upvotes · 0 comments --- Tags: [#numpy](https://daily.dev/tags/numpy) [View this post on daily.dev](https://daily.dev/posts/analog-ai-is-back-but-can-it-survive-its-own-noise--ajhaozm0p) ```json {"@context":"https://schema.org","@graph":[{"@type":"Organization","@id":"https://daily.dev/#organization","name":"daily.dev","url":"https://daily.dev","logo":{"@type":"ImageObject","url":"https://daily.dev/apple-touch-icon.png","width":180,"height":180},"sameAs":["https://twitter.com/dailydotdev","https://github.com/dailydotdev","https://www.linkedin.com/company/daily-dev-ltd"]},{"@type":"WebSite","@id":"https://daily.dev/#website","url":"https://daily.dev","name":"daily.dev","publisher":{"@id":"https://daily.dev/#organization"},"potentialAction":{"@type":"SearchAction","target":{"@type":"EntryPoint","urlTemplate":"https://daily.dev/search?q={search_term_string}"},"query-input":"required name=search_term_string"}}]} {"@context":"https://schema.org","@type":"TechArticle","headline":"Analog AI Is Back, But Can It Survive Its Own Noise?","url":"https://daily.dev/posts/analog-ai-is-back-but-can-it-survive-its-own-noise--ajhaozm0p","mainEntityOfPage":{"@type":"WebPage","@id":"https://daily.dev/posts/analog-ai-is-back-but-can-it-survive-its-own-noise--ajhaozm0p"},"datePublished":"2026-07-17T12:24:52.649Z","dateModified":"2026-07-17T12:25:21.514Z","description":"AI's energy demands are reviving analog computing, which uses physical properties like conductance, Ohm's law, and Kirchhoff's current law to perform...","image":"https://media.daily.dev/image/upload/f_auto,q_auto/v1/posts/15d413c9d50f7bb0bc5a00ca802024f2?_a=AQAEuop","thumbnailUrl":"https://media.daily.dev/image/upload/f_auto,q_auto/v1/posts/15d413c9d50f7bb0bc5a00ca802024f2?_a=AQAEuop","isAccessibleForFree":true,"articleSection":"Towards Data Science","inLanguage":"en","publisher":{"@type":"Organization","name":"daily.dev","url":"https://daily.dev","logo":{"@type":"ImageObject","url":"https://daily.dev/apple-touch-icon.png","width":180,"height":180}},"author":{"@type":"Organization","name":"Towards Data Science","logo":"https://media.daily.dev/image/upload/t_logo,f_auto/v1/logos/tds","url":"https://daily.dev/sources/tds"},"commentCount":0,"discussionUrl":"https://daily.dev/posts/analog-ai-is-back-but-can-it-survive-its-own-noise--ajhaozm0p","interactionStatistic":[{"@type":"InteractionCounter","interactionType":{"@type":"LikeAction"},"userInteractionCount":0},{"@type":"InteractionCounter","interactionType":{"@type":"CommentAction"},"userInteractionCount":0}],"keywords":"numpy","timeRequired":"PT10M"} {"@context":"https://schema.org","@type":"BreadcrumbList","itemListElement":[{"@type":"ListItem","position":1,"name":"Home","item":"https://daily.dev"},{"@type":"ListItem","position":2,"name":"Towards Data Science","item":"https://daily.dev/sources/tds"},{"@type":"ListItem","position":3,"name":"Analog AI Is Back, But Can It Survive Its Own Noise?"}]} ```