Bringing AI to Life at the Edge

by

Author: Denis Avetisyan

A new FPGA-accelerated framework unlocks the potential for real-time physical AI by drastically improving energy efficiency and performance on resource-constrained devices.

An FPGA acceleration strategy leverages a neural flow-based equivalent architecture to efficiently solve neural ordinary differential equations.
An FPGA acceleration strategy leverages a neural flow-based equivalent architecture to efficiently solve neural ordinary differential equations.

MERINDA enables rapid model recovery for complex physical systems, paving the way for intelligent edge computing applications.

Real-time understanding of physical systems is crucial for autonomous operation, yet current model recovery techniques often demand excessive computational resources. This work, ‘Enabling Physical AI at the Edge: Hardware-Accelerated Recovery of System Dynamics’, introduces MERINDA, an FPGA-accelerated framework that dramatically improves the energy efficiency and performance of model recovery. By replacing computationally expensive neural ODEs with a hardware-friendly, parallelizable formulation, MERINDA achieves 114× lower energy consumption and 28× smaller memory footprint compared to GPU implementations-while maintaining state-of-the-art accuracy. Could this approach unlock truly real-time, explainable physical AI for resource-constrained edge devices and critical autonomous applications?

The Challenge of Scale and Interpretability

While Large Language Models (LLMs) consistently demonstrate remarkable capabilities in natural language processing, their sheer size presents significant obstacles. These models often contain billions, and even trillions, of parameters – the values adjusted during training that encode the model’s knowledge. This immense scale demands substantial computational resources for both training and deployment, effectively limiting access to organizations with significant infrastructure. Beyond the practical difficulties, the very complexity of these models hinders understanding; the intricate interplay of so many parameters makes it difficult to discern why a model arrives at a particular conclusion, creating a “black box” effect. This lack of interpretability raises concerns regarding bias, reliability, and the potential for unintended consequences, prompting researchers to explore more efficient and transparent alternatives.

The power of Large Language Models often comes at a significant computational cost, largely due to the mechanisms enabling them to process information. Traditional attention mechanisms, such as Autoregressive Attention, excel at capturing relationships within sequential data, but their efficiency drastically diminishes as the length of those sequences increases. This is because calculating attention requires comparing each element in the sequence to every other element – a process that scales quadratically with sequence length O(n^2). Consequently, processing long documents, extended conversations, or high-resolution images quickly becomes impractical, limiting the ability of these models to tackle real-world complexities and demanding more efficient alternatives for scalable reasoning.

The prevailing reliance on attention mechanisms within large language models, while powerful, presents significant obstacles to both computational efficiency and genuine interpretability. As models grapple with increasingly complex tasks and longer sequences, the quadratic scaling of attention – requiring processing power proportional to the square of the input length – becomes a critical bottleneck. Consequently, researchers are actively exploring alternatives that move beyond attention’s all-to-all comparisons. These emerging approaches aim to approximate attention’s capabilities with linear or sub-quadratic complexity, enabling faster processing and deployment on resource-constrained devices. More importantly, a departure from attention offers the potential to unlock more transparent reasoning processes, allowing for a clearer understanding of how a model arrives at a particular conclusion, rather than simply observing that it does. This shift towards interpretable architectures is vital for building trust and ensuring responsible application of increasingly sophisticated artificial intelligence systems.

MERINDA optimizes neural network training by replacing iterative ODE solvers with a GRU-based neural flow and leveraging fine-grained spatial parallelism on FPGAs to accelerate computation.
MERINDA optimizes neural network training by replacing iterative ODE solvers with a GRU-based neural flow and leveraging fine-grained spatial parallelism on FPGAs to accelerate computation.

Unveiling System Dynamics: The Promise of Model Recovery

Model Recovery (MR) represents a departure from traditional system identification techniques by explicitly seeking to determine the governing equations of a dynamic system directly from observed data. Instead of estimating parameters within a pre-defined model structure, MR aims to uncover the functional relationships – often expressed as \frac{dx}{dt} = f(x, t) – that describe the system’s evolution. This approach is particularly valuable when the underlying equations are unknown or when a comprehensive understanding of the system’s dynamics is desired, offering the potential to reveal fundamental principles rather than simply approximating observed behavior. Successful MR yields a mathematical model capable of both replicating existing data and predicting future states, providing a more interpretable and generalizable representation than purely data-driven approaches.

Model recovery’s efficacy is predicated on the principle of identifiability, which dictates that a model’s parameters can be uniquely determined from observed data. Specifically, an identifiable model possesses the property that different parameter values will consistently produce different observable outputs given the same input data and noise distribution. If a model is not identifiable – meaning multiple parameter sets can generate equally plausible data – then the recovery process will be ambiguous and unable to converge on a single, definitive solution. This necessitates careful model selection and potentially the incorporation of prior knowledge or constraints to ensure parameters are uniquely estimable from the available data; otherwise, the recovered model may represent only one of many equally valid possibilities.

The application of Model Recovery (MR) frequently necessitates the iterative solution of Ordinary Differential Equations (ODEs) to estimate model parameters from observed data. This iterative process, while fundamental to MR, introduces significant computational cost, particularly when employing Neural ODEs (NODEs). NODEs represent a class of models where the derivative is defined by a neural network, requiring repeated evaluation of this network during the ODE solving process. The computational burden scales with the complexity of both the ODE system and the neural network architecture used within the NODE, as well as the length of the time series data being analyzed. Consequently, MR with NODEs demands substantial computational resources and can be time-consuming, especially for high-dimensional systems or long observation periods.

MERINDA employs a gated recurrent unit (GRU) neural network-based model-reference architecture for control.
MERINDA employs a gated recurrent unit (GRU) neural network-based model-reference architecture for control.

MERINDA: Accelerating Model Recovery with FPGA Implementation

MERINDA is a newly developed framework for accelerating Model Recovery (MR) through the implementation of Field-Programmable Gate Arrays (FPGAs). Unlike traditional approaches reliant on general-purpose processors like GPUs, MERINDA directly maps the MR process onto the reconfigurable hardware of FPGAs. This allows for customized dataflow architectures optimized for the specific computational demands of MR, resulting in substantial gains in both performance and energy efficiency. The framework is designed to address the high computational cost associated with MR tasks, offering a potential solution for resource-constrained environments and large-scale model recovery applications.

MERINDA addresses computational bottlenecks in Model Recovery by substituting traditional NODE layers with recurrent neural network (RNN) architectures based on Neural Flows. This implementation utilizes Gated Recurrent Unit (GRU) cells, selected for their efficiency in processing sequential data while maintaining performance comparable to more complex RNN variants. GRU cells offer a streamlined structure with fewer parameters than Long Short-Term Memory (LSTM) networks, reducing computational load and memory requirements. By employing this approach, MERINDA achieves a significant reduction in the complexity associated with NODE layer computations, thereby accelerating the overall Model Recovery process.

Performance evaluations demonstrate that the MERINDA framework, through FPGA implementation, significantly improves efficiency compared to traditional GPU-based model recovery. Testing indicates a 2.96x speedup, completing training in 88.5 seconds versus 149.14 seconds on a GPU. Furthermore, MERINDA achieves up to 114x energy reduction, consuming 434.09 Joules compared to the 49,375.12 Joules required by the GPU implementation during the same training process. These results highlight the potential of FPGA acceleration for reducing both the time and energy costs associated with model recovery tasks.

MERINDA utilizes Dense Neural Layers in conjunction with Mixed-Integer Linear Programming (MILP) to ensure model invertibility, a critical requirement for Model Recovery (MR). The MILP component is employed for optimized allocation of tasks to specific hardware resources within the FPGA, maximizing computational efficiency. This allocation strategy considers both the computational demands of each layer and the available resources on the FPGA, enabling efficient parallelization and minimizing execution time. The combination of dense layers and MILP-driven task allocation facilitates the reconstruction of input data from model outputs, a core function of the MERINDA framework.

Towards Sustainable AI: Edge Deployment and Sparse Models

MERINDA facilitates the implementation of Model Recovery directly on Edge AI devices through the utilization of Field-Programmable Gate Arrays (FPGAs). This architectural choice is significant as it moves the process of interpreting AI decision-making – previously confined to centralized servers – closer to the point of data generation. By performing Model Recovery on the edge, MERINDA reduces latency and enhances data privacy, as sensitive information doesn’t need to be transmitted for analysis. The framework’s ability to deploy interpretable AI on resource-constrained devices opens doors for applications in areas like autonomous vehicles, personalized healthcare, and industrial automation, where real-time insights and localized processing are paramount. This distributed approach not only improves responsiveness but also fosters greater trust and accountability in AI systems by making their internal logic more accessible at the source.

The MERINDA framework prioritizes the development of Sparse Models, a technique centered on minimizing the number of active non-linear terms within a neural network. This strategic reduction in model complexity directly translates to significant gains in computational efficiency and a dramatically lowered memory footprint. Evaluations demonstrate MERINDA’s ability to operate with a mere 214.23 MB of DRAM, a substantial improvement representing a 28-fold decrease compared to the 6118.36 MB demanded by a conventional GPU implementation. By focusing on these streamlined models, MERINDA effectively addresses the resource constraints often encountered in edge computing environments, fostering the potential for wider deployment of AI applications on devices with limited power and memory.

The MERINDA framework, while dramatically improving computational efficiency, demonstrates a slight trade-off in model accuracy. Evaluations reveal a Mean Squared Error (MSE) of 3.2965, representing a marginal increase compared to the 1.00 MSE achieved by a conventional GPU implementation. This nuanced result highlights the inherent challenges in balancing model complexity with resource constraints; MERINDA prioritizes reduced memory footprint and accelerated processing via Field Programmable Gate Arrays (FPGAs), accepting a small degree of increased error in the process. Researchers acknowledge this accuracy difference as a key area for future optimization, suggesting that further refinements to the model recovery process could minimize the performance gap while preserving the significant gains in energy efficiency and deployability at the edge.

The convergence of dedicated hardware acceleration and streamlined model architectures represents a pivotal step toward realizing truly sustainable and scalable artificial intelligence. By shifting computationally intensive tasks from power-hungry general-purpose processors to energy-efficient Field Programmable Gate Arrays (FPGAs), and simultaneously reducing model complexity through techniques like sparsity, MERINDA demonstrates a pathway for deploying AI solutions at the network edge with dramatically reduced resource demands. This approach not only lowers operational costs and environmental impact but also enables the proliferation of AI into resource-constrained environments and applications, fostering innovation beyond the limitations of centralized cloud computing and paving the way for a future where intelligent systems are ubiquitous and readily accessible.

The pursuit of efficient model recovery, as demonstrated by MERINDA, echoes a fundamental principle of system design. The framework’s FPGA acceleration isn’t merely about speed; it’s about recognizing that optimized hardware directly shapes behavioral outcomes. This aligns with the notion that structure dictates behavior. As Carl Friedrich Gauss observed, “If I have to explain something to you twice, it means I failed to explain it well enough the first time.” Similarly, MERINDA’s elegant implementation minimizes the need for iterative refinement, presenting a clear and concise solution to the challenges of deploying physical AI at the edge. The focus on energy efficiency highlights the importance of a well-defined structure for achieving optimal performance and minimizing waste.

What Lies Ahead?

The acceleration of model recovery, as demonstrated by MERINDA, shifts the focus from algorithmic novelty to systemic constraints. Achieving computational efficiency is, of course, merely a prelude; the true challenge resides in defining the appropriate level of abstraction for these embodied intelligence systems. Current approaches still largely treat dynamics as an external ‘thing’ to be modeled, rather than emergent properties of the interaction between hardware and environment. A complete solution necessitates co-design, where the model itself is shaped by the limitations – and opportunities – presented by the physical substrate.

Further investigation must address the question of robustness. This framework excels at recovering known dynamics, but real-world systems are rarely static. Adaptability to unforeseen disturbances, and the ability to learn from them without catastrophic failure, remain significant hurdles. The energy gains achieved are valuable, yet ultimately inconsequential if the system cannot reliably maintain coherence in a complex and unpredictable world. The long-term implications of embedding increasingly sophisticated models into resource-constrained edge devices also demands careful consideration.

Ultimately, the architecture of these systems will determine their ultimate utility. Good architecture is invisible until it breaks, and only then is the true cost of decisions visible.

Original article: https://arxiv.org/pdf/2512.23767.pdf

Contact the author: https://www.linkedin.com/in/avetisyan/

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2026-01-04 02:19