Edge AI Deployment Beyond Models: A BSP-Aware Systems Framework for Industrial Embedded Platforms
The paper presents a framework for deploying Edge AI in industrial embedded platforms, emphasizing the importance of a systems approach. It critiques the common practice of focusing on models first, which can lead to challenges in deployment. The proposed BSP-aware framework consists of five layers that aim to improve deployment outcomes such as reliability and throughput.
- ▪Industrial Edge AI programs often prioritize model development over platform considerations.
- ▪The paper argues for a systems approach to Edge AI deployment, addressing the complexities of embedded systems.
- ▪A BSP-aware framework is introduced, organized into five layers to enhance deployment effectiveness.
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| Original publisher | arXiv cs.AI |
| Canonical URL | https://arxiv.org/abs/2605.26119 |
| Publication time | Wed, 27 May 2026 00:00:00 -0400 |
| Retrieval time | 2026-05-27T04:07:56.398Z |
| Last seen | 2026-05-27T04:07:56.398Z |
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| Summary source text | contentText |
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| Commercial reuse | May the content be reused commercially? | Not permitted |
Basis: Derived from the published RSS/Atom feed. Contact: [email protected]. Reviewed: 2026-07-24.
Opening excerpt (first ~120 words) tap to expand
Computer Science > Distributed, Parallel, and Cluster Computing arXiv:2605.26119 (cs) [Submitted on 20 Apr 2026] Title:Edge AI Deployment Beyond Models: A BSP-Aware Systems Framework for Industrial Embedded Platforms Authors:Pitchai Muthu M View a PDF of the paper titled Edge AI Deployment Beyond Models: A BSP-Aware Systems Framework for Industrial Embedded Platforms, by Pitchai Muthu M View PDF Abstract:Industrial Edge AI programs often begin with the model and only later confront the platform. That sequencing is attractive because it allows early demonstrations, but it breaks down when the deployment target is an embedded system with long product lifecycles, vendor-specific kernels, heterogeneous accelerators, safety constraints, and nontrivial I/O paths.
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Excerpt limited to ~120 words for fair-use compliance. The full article is at arXiv cs.AI.
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