ALADIN: Accuracy-Latency-Aware Design-space Inference Analysis for Embedded AI Accelerators

• URL: http://arxiv.org/abs/2603.08722v1
• Date: Thu, 12 Feb 2026 13:19:44 GMT
• Title: ALADIN: Accuracy-Latency-Aware Design-space Inference Analysis for Embedded AI Accelerators
• Abstract summary: ALADIN is an accuracy-aware design-space inference analysis framework for mixed-precision quantized neural networks (QNNs)<n>It enables the evaluation and analysis of inference bottlenecks and design trade-offs across accuracy, latency, and resource consumption without requiring deployment on the target platform.<n>ALADIN is validated using a cycle-accurate simulator of a RISC-V based platform specialized for AI workloads.
• Score: 0.0
• License: http://creativecommons.org/licenses/by-nc-nd/4.0/
• Abstract: The inference of deep neural networks (DNNs) on resource-constrained embedded systems introduces non-trivial trade-offs among model accuracy, computational latency, and hardware limitations, particularly when real-time constraints must be satisfied. This paper presents ALADIN, an accuracy-latency-aware design-space inference analysis framework for mixed-precision quantized neural networks (QNNs) targeting scratchpad-based AI accelerators. ALADIN enables the evaluation and analysis of inference bottlenecks and design trade-offs across accuracy, latency, and resource consumption without requiring deployment on the target platform, thereby significantly reducing development time and cost. The framework introduces a progressive refinement process that transforms a canonical QONNX model into platform-aware representations by integrating both platform-independent implementation details and hardware-specific characteristics. ALADIN is validated using a cycle-accurate simulator of a RISC-V based platform specialized for AI workloads, demonstrating its effectiveness as a tool for quantitative inference analysis and hardware-software co-design. Experimental results highlight how architectural decisions and mixed-precision quantization strategies impact accuracy, latency, and resource usage, and show that these effects can be precisely evaluated and compared using ALADIN, while also revealing subtle optimization tensions.

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