A Practical Research Plan for Neural Network Quantization

As AI models continue to grow in complexity, efficient deployment has become as critical as model training itself. At Efaimo AI, we are focused on making powerful models practical for real-world hardware—from commodity GPUs to resource-constrained edge and mobile devices.

This document outlines our research plan, not a final results report. The experiments described herein are planned but not yet executed. Instead, this post lays out:

• Why quantization is critical.
• Our implementation and testing strategy.
• How we will evaluate the accuracy vs. efficiency trade-off.
• The deliverables we aim to produce (code, benchmarks, and technical articles).

A separate post will provide a conceptual deep dive into symmetric vs asymmetric linear quantization, focusing on theory and intuition rather than experimental results.

1. Why Quantization Matters

Modern neural networks present several significant deployment challenges:

• Memory Footprint: Large models can require hundreds of MBs, or even GBs, of storage.
• Inference Speed: FP32 computations are relatively expensive, especially on CPUs and edge devices lacking powerful tensor accelerators.
• Energy Consumption: High-precision operations consume more power, a critical bottleneck for mobile and embedded scenarios.
• Bandwidth Constraints: Downloading or streaming large FP32 models is slow and costly.

For a typical deep neural network, a single FP32 inference on standard hardware can easily take tens to hundreds of milliseconds per sample. Memory usage can limit batch size, throughput, or even whether the model fits on the device at all.

Quantization attacks these problems by reducing numerical precision. For example, converting from FP32 to INT8 yields:

• 4× smaller parameter size
• Improved cache locality
• Higher throughput on hardware that efficiently supports integer math
• Potentially lower energy consumption

The key research question is:

How far can we reduce precision (e.g., INT8, INT4) while maintaining acceptable levels of accuracy and stability for real-world tasks?

2. Scope and Assumptions

To ensure the first phase of this research remains focused and tractable, we will operate under the following assumptions:

• Model Families: Primary focus on small to medium CNNs (e.g., ResNet-18 / ResNet-34) on vision datasets. Optionally extend to ResNet-50 or lightweight transformers.
• Precision Targets: Primary focus on INT8 linear quantization (symmetric & asymmetric). Later phases explore mixed precision (INT8 + FP16/FP32).
• Quantization Type: Start with Post-Training Quantization (PTQ) for its simplicity. Escalate to Quantization-Aware Training (QAT) as needed.
• Deployment Targets: Commodity GPU + CPU for baseline. At least one edge-like environment.

All results and implementations will be shared through open-source PyTorch-based code, reproducible experiment scripts, and technical blog posts.

3. Research Goals

Our high-level goals are:

1. Quantify the Accuracy vs. Efficiency Trade-off — measure the accuracy delta between INT8 and FP32 against gains in memory, latency, and throughput.
2. Develop Practical Quantization Recipes — clear, practical examples for PTQ and QAT in PyTorch. Document when symmetric vs. asymmetric quantization is most effective.
3. Conduct Layer-wise Sensitivity Analysis — identify which layers are most fragile under quantization, and which can be aggressively quantized.
4. Produce Deployment-Ready Artifacts — publish quantized checkpoints and exportable models (e.g., ONNX / TorchScript).

Each stage is structured to produce independently useful artifacts, so even partial results yield valuable blog posts, diagrams, and code for the community.

4. Stage 1 — Establish FP32 Baselines

Before addressing quantization, we must establish clean, reproducible FP32 baselines.

4.1. Tasks and Datasets

• Image classification (e.g., CIFAR-10 / CIFAR-100, or a similar mid-size dataset).
• Clear train/validation/test splits.
• Standard data augmentation pipelines.

4.2. Deliverables

• Training scripts for a small benchmark CNN and ResNet-18 (optionally ResNet-34).
• Logged metrics: train / validation accuracy curves, final test accuracy, inference latency on GPU and CPU.
• A brief technical write-up covering hyperparameters, training techniques, and stability notes.

At the end of Stage 1, we will have reliable FP32 checkpoints to serve as the reference for all subsequent comparisons.

5. Stage 2 — Linear INT8 Quantization (PTQ)

Stage 2 implements Post-Training INT8 Quantization (PTQ) using linear quantization.

5.1. Symmetric Quantization

• Target: weights in conv/linear layers; normalized activations.
• Granularity: per-tensor first; explore per-channel if accuracy dictates.
• Experiments: FP32 vs. symmetric INT8 on ResNet-18 and small CNN. Report Top-1 accuracy, latency, model size.

5.2. Asymmetric Quantization

• Motivation: many activations (post-ReLU) are non-negative and highly skewed.
• Plan: apply asymmetric INT8 specifically to skewed activations; keep symmetric for weights and normalized pre-activations.
• Experiments: compare symmetric-only, mixed (sym weights + sym activations), and mixed (sym weights + asymmetric activations where appropriate).

5.3. Calibration Strategy

• Use a held-out calibration set (a few thousand samples).
• Evaluate naive min-max, percentile-based clipping (e.g., 99.9th percentile), and layer-wise vs. global calibration.

5.4. Deliverables

• Reusable calibration utilities.
• Quantization parameter configs (JSON/YAML).
• A blog post summarizing findings on symmetric vs. asymmetric, per-tensor vs. per-channel, and calibration strategies.

6. Stage 3 — Quantization-Aware Training (QAT)

If PTQ yields an unacceptable accuracy drop, we escalate to Quantization-Aware Training.

6.1. QAT Objectives

Insert “fake quantization” modules into the training graph; fine-tune the model so weights adapt to quantization noise; quantitatively compare FP32 / PTQ-only / QAT.

6.2. Experimental Design

• Use the best PTQ configuration as the starting point.
• Fine-tune for 10–30 epochs with low learning rates and careful stability monitoring.
• Optionally explore freezing batch norm statistics and gradual quantization schedules.

6.3. Deliverables

• QAT training scripts.
• Comparison plots (accuracy vs. epochs) for FP32 / PTQ / QAT.
• Layer-wise sensitivity analysis.

7. Stage 4 — Mixed Precision and Deployment

Once a robust INT8 pipeline is established, we will extend to mixed precision and practical deployment scenarios.

7.1. Mixed Precision Strategies

• Keep sensitive layers (e.g., final classifier) in FP16 or FP32.
• Use INT8 for most conv/linear layers.
• Explore INT8 weights + FP16 activations.
• Identify “good enough” mixes that meet strict latency/memory budgets while staying close to FP32 accuracy.

7.2. Deployment Targets

• Export to ONNX / TorchScript.
• Benchmark on CPU-only, representative GPU, and constrained environments.
• Deliverables: export scripts, reusable benchmark harness, deployment guide.

8. Evaluation Protocols

1. Metrics: Accuracy (Top-1 / Top-5), latency (ms/sample), throughput (samples/sec), memory (model file size, peak RAM/VRAM).
2. Setup: clearly document all hardware/software versions; report mean ± variance over repeated runs.
3. Reproducibility: fixed random seeds, locked dependencies, config-driven experiment scripts.

9. Milestones and Planned Posts

1. (This Post) Neural Network Quantization Research Plan
2. Concept Post: Symmetric vs. Asymmetric Linear Quantization
3. PTQ Results Post(s): FP32 vs. INT8 benchmarks; calibration deep dive.
4. QAT & Mixed Precision Post(s): QAT vs. PTQ analysis; practical recipes; deployment benchmarks.

10. Why This Research Matters

The importance of this research extends far beyond academic curiosity. Efficient neural networks enable:

• Edge Deployment: powerful models on phones, sensors, and low-power devices.
• Faster Iteration: lighter models allow researchers and engineers to iterate more quickly.
• Sustainability: at scale, reduced precision translates into significant energy and cost savings.
• Accessibility: more people can run, and build upon, advanced models using commodity hardware.

At Efaimo AI, our goal is not merely to inch accuracy numbers higher, but to make powerful models practical. This research plan is our roadmap; the true value will be realized in the concrete, reproducible experiments and tools we build and share along the way.