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
arXiv:2606. 29518v1 Announce Type: cross Abstract: With the widespread adoption of AI in various IoT scenarios such as smart sensing and processing, AI chips have become a common component at the edge.
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On Hardware-Aware Design and Optimization of Edge Intelligence
arXiv:2607. 16297v1 Announce Type: cross Abstract: Edge intelligence systems, the intersection of edge computing and artificial intelligence (AI), are pushing the frontier of AI applications.
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Low-Energy Reduced RISC-V Instruction Subset Processor for Tsetlin Machine Inference at the Edge
arXiv:2606. 19964v1 Announce Type: new Abstract: Tsetlin Machine (TM) is a logic-based machine learning approach that relies on simple bitwise operations and finite-state automata, which makes it attractive for edge AI deployments.
arXiv AI
Aug 18
Large Models for Small Devices: Recent Advances and Empirical Analysis of Edge AI Deployment
arXiv:2608. 15693v1 Announce Type: new Abstract: Running large AI models on resource-constrained edge devices requires model compression to reduce model size and computation.
Hugging Face Trending Papers
Jun 28
Harvesting AI Computation at the Edge via Generic Approximation
With the widespread adoption of AI in various IoT scenarios such as smart sensing and processing, AI chips have become a common component at the edge. These chips are typically specialized for structured neural network (NN) processing and are designed to meet peak workload demands.