EdgeProfiler: Frameworks for Automated Edge Profiling

• EdgeProfiler is a framework providing automated edge selection and profiling, with applications from MTF quantification to optical and statistical boundary estimation.
• It employs GPU-accelerated algorithms, data-parallel execution, and analytical models to rank and measure edges for optimal imaging and LLM performance.
• The methodologies optimize quantization, parameter tuning, and bias correction across diverse domains, enabling scalable and efficient system analysis.

EdgeProfiler denotes several distinct, technically rigorous frameworks and algorithms developed for optimal edge selection, profiling, and system evaluation in a variety of domains, including imaging system characterization, computational optics, statistical boundary estimation, and hardware-aware profiling of LLMs on edge devices. Despite context-specific implementations, all EdgeProfiler variants share the common principle of automating and quantifying edge selection and analysis, relying on analytical models, optimal ranking criteria, and data-parallel computation.

1. EdgeProfiler in Imaging: Automated Edge Selection for MTF Quantification

EdgeProfiler was introduced as a GPU-accelerated algorithmic framework for ranking and profiling edges in large-scale images to automate the Modulation Transfer Function (MTF) measurement—an essential criterion for imaging system sharpness. Manual selection of suitable edges is infeasible for gigabyte-scale images, prompting the need for robust automation (Dasgupta, 2015).

The method evaluates each edge according to three metrics:

• Length ($l$): The pixel count of a connected edge.
• Thickness ($t$): Mean or maximum edge width, with ideal sharp edges exhibiting $t \approx 1$.
• Orientation angle ($\theta$): The acute angle (in radians) between the edge direction and the $x$-axis, determined via image gradients.

The orientation is computed by first calculating the gradients $G_x(x, y) = \frac{\partial f}{\partial x}$ and $G_y(x, y) = \frac{\partial f}{\partial y}$, yielding a local orientation $\alpha(x, y) = \arctan(G_y / G_x)$ and a perpendicular edge angle $\theta(x, y) = \alpha(x, y) + \pi/2$.

An exact ranking function is defined as:

$$R = \frac{l - t}{10} + |\theta|, \quad l, t \in \mathbb{Z}^+,\, 0 \leq \theta \leq \frac{\pi}{2}.$$

Edges are detected using a 5×5 Laplacian-of-Gaussian (LoG) kernel, thresholded to a binary map, and labeled for connectivity. Edge thickness is estimated (e.g., by morphological thinning) and orientation averaged along the segment. The edge that maximizes $t$0 is selected for profiling.

Subsequently, the Edge Spread Function (ESF) is sampled along the edge normal, the Line Spread Function (LSF) is computed via numerical differentiation, and the MTF is obtained by taking the modulus of the discrete Fourier transform of the LSF. All operations are parallelized across GPU tiles, leveraging data-parallel kernels and optimized memory management, resulting in reported “tremendous speed-up” for large imagery.

2. Analytical Model Extensions and Optical Profiling Applications

EdgeProfiler principles extend to fine-grained optical profiling, as in the case of knife-edge-based profiling of tightly focused light beams. Here, the interaction between a polarized Gaussian beam and a sub-wavelength knife-pad on a substrate is modeled analytically, decomposing the transmission into spectral components and Fresnel coefficients. The measured current is given by a convolution integral over the field distribution, with the transmission functions $t$1 and $t$2 Taylor-expanded in spatial frequency. The resulting expansion introduces a series of coefficients $t$3, governing not only the projected field intensity but also higher-order derivatives, encapsulating polarization-dependent centroid shifts and width distortions. These systematic corrections underpin substrate- and polarization-sensitive implementations of EdgeProfiler in photonic metrology (Huber et al., 2016).

3. Series-Projection EdgeProfiler for Statistical Boundary Estimation

EdgeProfiler additionally refers to a methodology for statistical boundary estimation in bounded planar domains, as formulated for point processes on supports of form $t$4 (Girard et al., 2011).

Given a homogeneous Poisson sample in $t$5, the unknown boundary function $t$6 is estimated through the following steps:

• Partition $t$7 into $t$8 subintervals, collecting the maximal ordinate $t$9 in each.
• Select a $t \approx 1$0 orthonormal basis $t \approx 1$1 of $t \approx 1$2, and define the Dirichlet kernel $t \approx 1$3.
• Form the raw projection estimator:

$t \approx 1$4

• Address negative bias by including minimal-point extremes $t \approx 1$5, yielding the bias-corrected estimator

$t \approx 1$6

This estimator exhibits mean integrated squared error convergence and asymptotic pointwise normality under explicit growth conditions on $t \approx 1$7 and $t \approx 1$8, with rates quantified for trigonometric and B-spline bases.

4. EdgeProfiler for Performance Evaluation of Lightweight LLMs on Edge Platforms

EdgeProfiler also designates a fast analytical-model–driven profiling framework for estimating latency, memory footprint, throughput, and energy of lightweight transformer-based LLMs on resource-constrained edge hardware. The model integrates device parameters (compute and I/O bandwidths, utilization factors), model configuration (layer count $t \approx 1$9, hidden size $\theta$0, vocabulary $\theta$1, etc.), and quantization precision ($\theta$2 bytes per parameter), producing closed-form predictions for parameter count, FLOPs per token, and memory footprint:

• Parameter count:

$\theta$3

• FLOPs per token:

$\theta$4

• Memory footprint:

$\theta$5

Detailed per-stage latencies—compute, memory, storage, H2D, and network—are summed (with partial overlap depending on hardware) to estimate end-to-end inference time per token. Energy per token is modeled as

$\theta$6

with $\theta$7 and $\theta$8 (Joules per operation/byte) hardware-calibrated.

Aggressive 4-bit quantization (INT4) of weights is implemented as

$\theta$9

with per-tensor scaling $x$0 via MSE minimization, reducing memory consumption by 60–70% and energy use by 35–50% versus FP16, while inference speed increases 2–3× and accuracy decreases by only 2–5% relative to FP16 baselines. Benchmarks on Raspberry Pi and Jetson platforms are provided for several LLMs, systematically quantifying trade-offs among accuracy, memory, speed, and energy (Pinnock et al., 6 Jun 2025).

5. Methodological and Computational Considerations

EdgeProfiler frameworks commonly employ the following computational strategies:

• Data partitioning: Tiling or cell decomposition, enabling scalable, memory-efficient processing for large images, point sets, or model parameters.
• Data-parallel execution: Native GPU mapping in imaging applications; concurrent memory and compute streams for LLM profiling.
• Analytical calibration: Model parameters and hardware utilization factors are empirically calibrated to, e.g., ensure analytical predictions match observed timings within 10–15%.
• Parameter tuning: Selection of kernel/basis cutoffs (e.g., LoG $x$1, Dirichlet kernel ranks $x$2, partition counts $x$3) guided by theoretical error and variance trade-offs.

In statistical boundary estimation, classic trade-offs emerge: bias-variance balance dictates $x$4 and $x$5, with simulation studies confirming $x$6 convergence rates for bias-corrected estimators (Girard et al., 2011).

6. Contextual Impact and Practical Guidelines

EdgeProfiler algorithms have substantively advanced the automation and scalability of edge selection and profiling in domains where manual curation is untenable, such as giga-pixel satellite image analysis and in-situ edge MTF profiling for imaging systems. For LLM deployment at the edge, analytical profiling frameworks such as EdgeProfiler enable systematic determination of optimal quantization and resource configurations, reducing deployment overhead.

Practical guidelines arising from the literature include:

• Optimize for long, sharp, vertical edges ($x$7, $x$8) for MTF characterization (Dasgupta, 2015).
• Select kernel and thresholding parameters to suppress noise yet preserve true edge geometry.
• In knife-edge optical profiling, substrate and pad selection critically determine polarization-dependent profiling errors; low-index glass and sub-skin-depth pad thicknesses are recommended to suppress systematic distortions (Huber et al., 2016).
• In statistical boundary estimation, validate basis sum growth and choose tuning parameters by minimizing cross-validated MISE.
• For edge LLMs, INT8 generally achieves a robust balance of speedup and accuracy, while INT4 can be employed where further memory and energy constraints dominate, accepting moderate accuracy loss (Pinnock et al., 6 Jun 2025).

EdgeProfiler’s analytical and data-driven methodologies thus provide a coherent toolkit for optimal, efficient profiling of edges and boundaries across imaging, optics, statistics, and hardware-aware machine learning.