HoliBench: A Cross-Platform Benchmarking and Deployment Toolkit for Foundation Models in CPS-IoT Applications · Research brief

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minimum test setup: not reported
published evidence setup: not reported
source-linked claims: 0 of 2
exposure checks: 0 of 8

Why it was shortlisted

The technical change, research area, and reason it entered the archive.

recommendation: test
research area: other
technical change: Cross-platform deployment benchmarking and profiling toolkit
published: 2026-09-11

The toolkit is directly usable on constrained infrastructure and its compose-once profiles can eliminate repeated profiling, but its value depends on whether the reader actually deploys across heterogeneous edge devices rather than a single GPU or APIs, so workload-specific validation is warranted before adoption.

Central equations

Original equations, typeset for reading. Paperraft explanations connect the mechanism, its aim, and the evidence.

The second stage is a pluggable solver. The solver interface accepts any implementation conforming to the contract solve(candidate_set, constraints, weights) $\to$ ranked_configurations, separating the calibrated measurement infrastructure (the profile table) from the selection policy applied to it. HoliBench ships with an Integer Linear Programming (ILP) solver as the representative implementation. The ILP defines a weighted utility $U_{i}$ over normalized metrics for each configuration $i \in 𝒞$ and selects the configuration maximizing $U_{i}$ , subject to the user-specified deployment constraints. Formally,

Maximize U_{i} = \sum_{j} w_{j} \hat{v}_{i, j} subject to:

equation (4) · §3. System Architecture · proposed method

l_{i} \leq L_{\max}, e_{i} \leq E_{\max}, m_{i} \leq M_{\max}, a_{i} \geq A_{\min}

unnumbered equation · §3. System Architecture · complexity

equations from arXiv HTML · fetched 2026-09-29

Claims and evidence

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claim 01Paperraft inference
Read the brief interpretation
HoliBench replaces ad-hoc, platform-specific profiling scripts and accuracy-only evaluation harnesses with a unified toolkit that measures accuracy, latency, and energy once per device and reuses those profiles for constraint-aware deployment selection. The cost is an upfront profiling campaign per device-model-backend combination, integration effort with its abstraction layer, and the engineering overhead of maintaining an open-source toolkit rather than a model-level optimization. It can fail if the reader's targets are only a single 24 GB GPU or third-party APIs (where cross-device calibration adds little), if profile composition breaks down under concurrent rather than sequential co-resident execution, or if measurement drift and quantization-backend interactions make reused profiles stale.
This is a Paperraft inference. It is not pinned to a verified PDF passage.
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claim 02Paperraft inference
Standalone single-model profiles predict multi-model pipeline latency within 1.2% and power within 2.5% under sequential co-resident execution; other findings are qualitative (e.g., quantization reduces latency only on low-precision hardware, autoregressive average power is approximately constant across output lengths).
This is a Paperraft inference. It is not pinned to a verified PDF passage.
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Exposure map

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quality

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compute and memory

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latency

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operations

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software compatibility

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security

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data and training

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reproducibility

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Inside the paper

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Provisional research brief updated 2026-10-18. Paperraft has not reproduced this experiment.