Source-linked AI summary
A Generalized Optimization Engine (GOE) for Edge AI Inference Acceleration
Venkat R. Dasari, Jakob A. Adams, Vinod K. Mishra, Brian Jalaian
TL;DR
AI models are costly to deploy on resource-constrained, heterogeneous edge hardware, motivating a generalized optimization approach. The paper proposes GOE, which unifies optimization methods and hardware/compiler abstractions across model scales. GOE-compressed language models run on a GPU-less edge CPU, while deployment accuracy depends on the compression method rather than nominal bit-width alone.
Problem
AI models, particularly transformer-based LLMs, are computationally complex to deploy on dynamic, heterogeneous, resource-constrained tactical-edge platforms.
Method
GOE unifies pruning, quantization, distillation, and compilation as constrained or multi-objective optimization over accuracy, latency, memory, and energy, with search decomposed by model scale.
Results
GOE-compressed language models deploy and run on a GPU-less edge CPU, while proper GGUF quantization preserves task accuracy and a naive same-bit-width scheme collapses it.
Takeaways & Limitations
Compression-method selection is central to deployment success because nominal bit-width alone does not determine whether compressed models work on edge hardware.
Takeaways & Limitations
GOE currently covers vision and language models, while extension to sensor fusion, multimodal data, and runtime adaptivity remains open.
Abstract
from arXiv · showhide
Artificial intelligence (AI) models have demonstrated remarkable capabilities across various domains, yet their widespread deployment is impeded by significant computational costs, particularly on resource-constrained devices. This paper explores the theoretical underpinnings of various AI model optimization techniques, algorithms, and abstractions, discussing their potential to reduce computational complexity, memory footprint, latency, and power consumption. Furthermore, we propose a comprehensive hardware (HW) and model-agnostic generalized optimization architecture that integrates these techniques for improved efficiency. Our study underscores the critical role of such a generalized optimization system in preparing model deployment over resource-constrained heterogeneous hardware in a tactical environment. As a concrete demonstration, we show that GOE-compressed language models deploy and run on a GPU-less edge CPU, and that the choice of compression method, not merely its nominal bit-width, determines whether task accuracy survives deployment.
1 Introduction
AI models, especially transformer-based LLMs, are difficult to deploy on dynamic, heterogeneous, resource-constrained tactical-edge platforms. Generalized optimization remains challenging because hardware, software, model, and accuracy constraints interact.
- Transformer-based LLMs are computationally complex and difficult to deploy on edge platforms.
- Tactical-edge systems require real-time sensing and decision support despite dynamic hardware and limited computing, memory, and energy resources.
- Generalized optimization must accommodate model diversity, hardware heterogeneity, network variation, and platform-specific libraries and frameworks.
- Compression, pruning, and neural architecture search reduce resource demands, but accuracy decay and narrow applicability remain concerns.
2 Related Research
Related research develops CNN and transformer-specific techniques to reduce inference cost. These approaches include sparse or linear-complexity attention and methods that transfer across model families, but their effectiveness depends on context.
- Optimization techniques developed for CNNs can also support LLM inference acceleration, while other methods target transformers exclusively.
- Sparse attention reduces computation by focusing on a subset of relevant elements, and dynamic sparse attention adjusts sparsity within the attention mechanism.
- Traditional attention has quadratic complexity in input size, whereas a proposed linear-complexity mechanism offers efficiency gains for large transformers.
- Existing optimization approaches each excel in specific contexts, motivating broader approaches to transformer efficiency.
3 Problem Formulation
The problem formulation treats deployment optimization as constrained or multi-objective search over model and hardware decisions. Objectives and budgets cover performance, compression, inference time, memory, energy, and computational power.
- GOE casts model optimization as constrained search over accuracy, latency, memory, and energy budgets for a target device.
- The single-objective formulation maximizes Performance(m, q, p, h) subject to an energy budget.
- The multi-objective formulation optimizes performance and CompressionRate(m, q, p, h) under the same energy constraint.
- For large models, tractability is addressed by restricting the search to a smaller set of optimization knobs.
4 Technical Approach
GOE combines model optimization methods with compiler support in an automated, hardware- and model-oriented pipeline. Its goal is to reduce computation and model size while accelerating inference on resource-constrained edge platforms.
- GOE combines model compression approaches and compilers in an automated end-to-end optimization pipeline.
- The pipeline adapts state-of-the-art optimization methods for deployment across resource-constrained edge environments.
- Its stated objectives are reducing computational complexity and model size while accelerating model inference.
4.1 Model Analyzer
The Model Analyzer is GOE’s entry point: it characterizes an input model, reports its key properties, and routes it to suitable compression modules.
- The Model Analyzer characterizes the input model to determine which optimization methods should be applied.
- The broader engine profiles the model before architecture-aware compression and compilation for an edge hardware target.
- Its analysis report includes layer count, parameter count, base sparsity, and data types.
- GOE uses the analysis information to route the model through one or more compression modules.
4.2 Pruning and Quantization
GOE combines pruning and quantization methods whose accuracy effects depend on model architecture, precision, hardware, and recovery procedures.
- Pruning can reduce model size but is architecture-sensitive, may damage accuracy, and typically requires fine-tuning for recovery.
- Quantization lowers precision and model size but can introduce errors, so the target hardware and accuracy-recovery strategy influence method selection.
- OBC supports unstructured, block, and N:M pruning, n-bit quantization, post-optimization tuning, and statistical correction for CNNs.
- Torch-Pruning uses DepGraph to preserve layer dependencies and tensor-dimension alignment during structured pruning.
- TorchAO provides one-shot post-training quantization without calibration or fine-tuning.
4.3 Neural Architecture Search
GOE uses weight-sharing supernets and evolutionary search to discover efficient architectures, making the approach suitable for CNNs and larger models where conventional NAS is impractical.
- GOE’s NAS combines supernets and self-distillation to discover efficient neural architectures.
- Progressive shrinking and CompOFA convert CNNs into weight-sharing supernets while reducing search space and training time.
- Evolutionary search generates, mutates, evaluates, and selects sub-architectures across successive generations under target constraints.
- The approach supports larger LLMs and Transformers, where NAS is often impractical because their search spaces are vast, while remaining applicable to CNNs.
4.4 Compilers
GOE includes a compiler module that adapts optimized models to heterogeneous edge hardware through hardware-specific and hardware-agnostic compilation paths.
- GOE compiles post-optimized models into forms compatible with heterogeneous target architectures.
- Torch.compile uses just-in-time compilation to transform PyTorch operations into optimized inference kernels.
- TensorRT targets Nvidia hardware and supports optional 2:4 pruning plus FP16 and INT8 quantization.
- ONNX provides a framework-independent neural-network format, while ONNXRuntime can execute ONNX inference when further compilation is unavailable.
- Apache TVM is CPU- and GPU-agnostic and supports automatic or hand-crafted compilation for target-device customization.
5 Results and Discussion
GOE combines model-scale-specific optimization routes and shared deployment constraints to compress CNNs and LLMs for resource-limited hardware. Results show that pruning, quantization, NAS, and their combinations can reduce size or improve throughput, but deployment accuracy depends strongly on the selected compression method.
- Optimization architecture: GOE splits optimization by model scale, using supernetwork-based NAS for compact CNNs and compression for larger LLMs.The design targets tactical settings with scarce onboard compute and power, while accounting for data scarcity and mission sensitivity.
- Structured pruning: Structured pruning compressed ResNet 50 and ViT by 25–75%, and five epochs of fine-tuning produced accuracies that met or exceeded the uncompressed models.The experiments report that the fine-tuning completed in less than 20 minutes.
- Quantization: TorchAO quantization delivered over 10x throughput gains for MobileNet models and over 25x for ResNet 50, with minimal accuracy impact except for MobileNet v3 Small.The passage attributes the size and latency benefits to hardware-supported quantization.
- Neural architecture search: One-shot NAS supports device-specific constraints, and the ResNet 50 search found top subnets with 2x compression while staying within a maximum of 4% accuracy loss.The selected subnet is not necessarily the largest or individually highest-performing candidate, so evaluating multiple top candidates supports deployment selection.
- NAS & quantization: Combining NAS with quantization retained 2x subnet compression and added another 10% model-size reduction with negligible accuracy loss.The pipeline applies quantization after finding an optimal subnet.
- Edge-CPU deployment: On a GPU-less edge CPU, Q8_0 was about 1.9x smaller and 1.7x faster with essentially unchanged accuracy, while Q4_K_M was roughly 3x smaller and 2.5x faster with a few points lost.A naive dynamic 8-bit method collapsed to chance accuracy, and aggressive pruning also approached chance despite speed and size gains.
6 Conclusion
GOE provides a model- and hardware-agnostic framework that unifies optimization for CNNs and LLMs under deployment constraints. Its edge-CPU demonstration shows that compression method, not nominal bit-width alone, determines whether language-model accuracy survives deployment, while multimodal coverage remains future work.
- Framework: GOE unifies pruning, quantization, distillation, and compilation into a model- and hardware-agnostic framework for resource-constrained deployment.The framework treats accuracy, latency, memory, and energy as shared optimization objectives or constraints.
- Framework: The framework supports tractable optimization across both CNNs and LLMs and connects its abstraction layer to existing compiler backends.This design targets heterogeneous deployment without requiring a separate optimization formulation for each model family.
- Demonstration: GOE-compressed language models deployed and ran on a GPU-less edge CPU, demonstrating deployment beyond GPU-equipped systems.The deployment demonstration directly tests whether compressed language models can operate on constrained edge hardware.
- Demonstration: Compression method determined deployment viability: proper GGUF quantization preserved task accuracy, whereas a naive scheme with the same bit-width collapsed it.The result makes deployability and retained accuracy more important than nominal precision width alone.
- Limitations: GOE currently covers vision and language models, but extending it to sensor fusion and multimodal data remains an open scope boundary.The conclusion also identifies runtime reconfiguration under changing compute or power availability as a future direction.