arXiv AI
A Generalized Optimization Engine (GOE) for Edge AI Inference Acceleration
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Optimizing AI Inference Across the Deployment Stack
The paper argues that AI deployment performance depends on interactions among compression, compiler transformations, and serving policies rather than just model architecture. It introduces a three‑layer taxonomy—model‑level techniques, compiler transformations, and system policies—and frames deployment as a constrained multi‑objective optimization problem over accuracy, latency, throughput, memory footprint, and energy. The authors propose an evidence protocol for comparable benchmarking and synthesize data from edge and data‑center platforms to show that cross‑layer interactions drive deployment outcomes, concluding with a constraint‑aware selection procedure and open research problems.
arXiv Machine Learning
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Harvesting AI Computation at the Edge via Generic Approximation
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On Hardware-Aware Design and Optimization of Edge Intelligence
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Low-Energy Reduced RISC-V Instruction Subset Processor for Tsetlin Machine Inference at the Edge
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Benchmarking Model Compression Techniques Across Heterogeneous Edge Platforms: A Predictive Maintenance Use Case for Rotating Machinery
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arXiv Computation and Language
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TrainBridge: Agentic NPU Training Optimization Guided by GPU Compilation Evidence
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