ZAC-Hardware: Joint Hardware Configuration and Collective Resource Provisioning for Embodied Intelligent Systems
Abstract
Embodied intelligent systems are commonly designed under the assumption that each agent should independently possess the hardware required to execute its anticipated artificial intelligence workloads. This design principle can lead to duplicated computing infrastructure, underutilized accelerators, excessive energy consumption, and high system-wide acquisition costs. Although distributed computing and task offloading can reduce the need for local execution, they do not necessarily reconsider the hardware configuration of each agent as a fundamental system-design decision.
This paper proposes ZAC-Hardware, a specialized research framework that integrates local hardware configuration and collective resource provisioning into a joint system-level optimization problem. Rather than equipping every embodied agent with sufficient hardware to independently execute its expected workloads, the framework determines which agents require high-performance computing resources, which can operate with basic local hardware, which should retain redundant computing capacity, and which resource requirements can be satisfied through services provided by other agents. Hardware configuration is therefore optimized jointly with task assignment, communication, energy consumption, service reliability, and failure recovery.
The framework introduces a heterogeneous embodied-agent model, hardware configuration variables, workload assignment variables, collective service relationships, lifecycle cost functions, and operational feasibility constraints. A mixed-integer optimization formulation is developed to represent hardware selection and task allocation, while extensions address uncertainty, time-varying demand, provider failures, and safety-critical local functions. The paper also establishes a comparative evaluation methodology involving fully provisioned, fixed-hardware offloading, heterogeneous hardware, and jointly optimized ZAC-Hardware configurations.
The proposed framework does not assume that reducing onboard hardware is always beneficial. Its central hypothesis is that jointly optimizing hardware configuration and collective provisioning can improve system-level efficiency when avoided local hardware costs exceed the additional costs and risks of collective services. The framework is presented as a research proposal requiring theoretical analysis, simulation, and physical testbed validation. Its contribution relative to existing work in hardware–software co-design, heterogeneous computing, cloud robotics, and distributed resource allocation must be established through systematic comparative evaluation.
Related
- ZAC-Specialization: Collective Capability Provisioning Through Hardware–Role Co-Design in Heterogeneous Intelligent Agent Populations
- ZLP-Hardware: A General Framework for Zero-Local-Provisioning of Hardware Resources and Components
- Beyond Individual Large Language Models: A General Framework for Intelligent and Autonomous Provisioning of AI Capabilities
- Intelligent P-cycle provisioning in elastic optical networks via energy-aware deep reinforcement learning
- Zero-Local-Provisioning for Training (ZLP-Training): A General Framework for Collective Provisioning of Training Capabilities
BibTeX
@misc{zhang2026zachardware,
title = {{ZAC-Hardware: Joint Hardware Configuration and Collective Resource Provisioning for Embodied Intelligent Systems}},
author = {Jincheng Zhang},
howpublished = {Zenodo},
year = {2026},
month = oct,
doi = {10.5281/zenodo.23450982},
url = {https://zenodo.org/records/23450982},
note = {CC BY 4.0},
}
RIS
TY - UNPB
TI - ZAC-Hardware: Joint Hardware Configuration and Collective Resource Provisioning for Embodied Intelligent Systems
AU - Jincheng Zhang
PY - 2026
DA - 2026/10/18
PB - Zenodo
DO - 10.5281/zenodo.23450982
UR - https://zenodo.org/records/23450982
AB - Embodied intelligent systems are commonly designed under the assumption that each agent should independently possess the hardware required to execute its anticipated artificial intelligence workloads. This design principle can lead to duplicated computing infrastructure, underutilized accelerators, excessive energy consumption, and high system-wide acquisition costs. Although distributed computing and task offloading can reduce the need for local execution, they do not necessarily reconsider the hardware configuration of each agent as a fundamental system-design decision. This paper proposes ZAC-Hardware, a specialized research framework that integrates local hardware configuration and collective resource provisioning into a joint system-level optimization problem. Rather than equipping every embodied agent with sufficient hardware to independently execute its expected workloads, the framework determines which agents require high-performance computing resources, which can operate with basic local hardware, which should retain redundant computing capacity, and which resource requirements can be satisfied through services provided by other agents. Hardware configuration is therefore optimized jointly with task assignment, communication, energy consumption, service reliability, and failure recovery. The framework introduces a heterogeneous embodied-agent model, hardware configuration variables, workload assignment variables, collective service relationships, lifecycle cost functions, and operational feasibility constraints. A mixed-integer optimization formulation is developed to represent hardware selection and task allocation, while extensions address uncertainty, time-varying demand, provider failures, and safety-critical local functions. The paper also establishes a comparative evaluation methodology involving fully provisioned, fixed-hardware offloading, heterogeneous hardware, and jointly optimized ZAC-Hardware configurations. The proposed framework does not assume that reducing onboard hardware is always beneficial. Its central hypothesis is that jointly optimizing hardware configuration and collective provisioning can improve system-level efficiency when avoided local hardware costs exceed the additional costs and risks of collective services. The framework is presented as a research proposal requiring theoretical analysis, simulation, and physical testbed validation. Its contribution relative to existing work in hardware–software co-design, heterogeneous computing, cloud robotics, and distributed resource allocation must be established through systematic comparative evaluation.
ER -