Model Quantization

Converts high-precision floating-point representations (FP32) to lower-bit formats such as INT8 or INT4.

• Reduces model size by up to 4x compared to FP32 baselines
• Enables deployment on microcontrollers and low-power FPGAs
• Typical target: 8-bit integer for balanced accuracy and speed
• Example: A 100MB FP32 model compresses to approximately 25MB at INT8

Low-precision arithmetic executes significantly faster on hardware with dedicated integer processing units.

• INT8 matrix multiplication runs 2-4x faster than FP32 on modern NPUs
• Reduces memory bandwidth bottleneck by transferring fewer bytes per inference
• Critical for real-time emitter classification on edge SDRs
• Example: Latency drops from 12ms to 3ms on NVIDIA Jetson with TensorRT INT8 calibration

Simulates low-precision effects during the training phase to minimize accuracy degradation.

• Inserts fake quantization nodes into the forward pass
• Model learns to adapt weights to quantization error
• Typically preserves 99%+ of baseline accuracy
• Essential when post-training quantization causes unacceptable drift
• Example: A ResNet-50 classifier maintains 76.1% top-1 accuracy with QAT versus 75.8% with PTQ

Assigns different numerical precisions to various layers based on sensitivity analysis.

• Sensitive layers (first/last) often retain FP16 or FP32 precision
• Robust intermediate layers aggressively quantized to INT4 or INT8
• Balances computational speed with minimal accuracy loss
• Implemented via tools like NVIDIA TensorRT and AMD Vitis AI
• Example: Quantizing only convolutional layers while keeping attention mechanisms at higher precision

Requires a representative calibration dataset to determine optimal quantization ranges.

• Captures activation distributions from real inference workloads
• Uses techniques like KL divergence or min-max calibration
• Accounts for hardware-specific integer saturation behavior
• Critical for avoiding clipping errors that degrade signal classification
• Example: Calibrating with 500 representative RF samples ensures accurate IQ constellation quantization

Quantization enables aggressive operator fusion by aligning compute kernels with reduced bit widths.

• Combines convolution + batch normalization + activation into single INT8 kernel
• Eliminates intermediate memory round-trips for activation tensors
• Reduces DRAM access by up to 60% on FPGA deployments
• Works alongside TensorRT and ONNX Runtime graph optimizations
• Example: Fusing a Conv2D-BatchNorm-ReLU block executes in one hardware clock cycle on Xilinx Zynq DPU

Deploying quantized INT8 models onto Xilinx Zynq or Kintex devices using Vitis AI or FINN. This allows the neural network to be synthesized directly into the programmable logic, achieving deterministic, ultra-low latency classification of IQ constellation distortions without a host CPU.

• Eliminates OS jitter for real-time physical layer authentication
• Achieves sub-millisecond inference latency on raw I/Q streams
• Utilizes AXI4-Stream interfaces for direct, zero-copy data ingestion from JESD204B converters

Leveraging TensorRT on an NVIDIA Jetson Orin to deploy INT8 calibrated models for automatic modulation classification. This balances the high throughput of a GPU with the power constraints of a remote spectrum monitoring node.

• Uses mixed-precision inference to keep sensitive layers at FP16
• Processes wideband direct RF sampling data in real-time
• Reduces memory footprint by 4x compared to FP32, mitigating the memory bandwidth bottleneck

Running post-training quantization to generate a TinyML model for an ARM Cortex-M microcontroller. This enables a low-power sensor to perform cyclostationary feature extraction and classification on a specific signal of interest, waking a higher-power processor only when a target is identified.

• Uses TensorFlow Lite Micro with an Edge TPU or bare-metal inference engine
• Operates on a pre-processed digital down converter output
• Enables years-long battery life for unattended ground sensors

Applying quantization-aware training (QAT) to a model designed to track and compensate for drift in device signatures caused by temperature and aging. By simulating 8-bit quantization during training, the deployed model on a Xilinx Zynq maintains high accuracy despite environmental changes.

• Ensures robust steady-state waveform fingerprinting over years of operation
• Fuses batch normalization layers for efficient operator fusion
• Maintains >99% of the floating-point model's identification accuracy

Using the ONNX Runtime with its built-in quantization tools to accelerate a channel-robust feature learning model on a general-purpose x86 SDR platform. This provides a hardware-agnostic path to real-time inference without custom FPGA synthesis.

• Leverages INT8 vector neural network instructions (VNNI) on modern Intel CPUs
• Applies post-training quantization to a PyTorch model exported to ONNX
• Ideal for rapid prototyping and software-defined radio applications

Executing a mixed-precision inference pipeline where a lightweight, fully quantized INT8 model performs initial screening for adversarial device spoofing, while a higher-precision FP16 model acts as a secondary arbiter for ambiguous cases.

• Balances power efficiency with high-confidence anomaly rejection
• Deployed on a NVIDIA Jetson using TensorRT's layer-wise precision control
• Reduces average power consumption while maintaining a strict security posture

Unlike post-training methods, QAT simulates low-precision arithmetic during the forward and backward passes. This allows the model to learn to compensate for quantization error.

• Maintains higher accuracy than PTQ at very low bit-widths (INT4 or lower)
• Inserts fake quantization nodes into the computational graph
• Essential for complex signal processing models where precision loss is unacceptable

A complementary technique that removes redundant parameters before or alongside quantization. Unstructured pruning zeroes out individual weights, while structured pruning removes entire channels or filters.

• Can achieve 90%+ sparsity with minimal accuracy impact
• Works synergistically with quantization for compound compression
• Structured pruning directly reduces memory bandwidth bottleneck pressure

A teacher-student training paradigm where a compact model learns to mimic the soft output distributions of a larger, high-precision model. The student often serves as the starting point for subsequent quantization.

• Captures dark knowledge lost in direct training
• Produces models that quantize more gracefully
• Frequently used for emitter classification models deployed on FPGAs

Rather than applying uniform INT8 precision across the entire network, mixed-precision assigns higher bit-widths to sensitive layers and lower precision to robust ones.

• First layers and final classification heads often retain FP16 or INT16
• Balances TOPS/Watt efficiency against signal fidelity requirements
• Critical for preserving subtle IQ constellation distortion features used in fingerprinting

A graph-level optimization that combines consecutive operations—such as convolution + batch normalization + activation—into a single kernel launch. This eliminates intermediate memory round-trips.

• Reduces inference latency by 20-40% on embedded GPUs
• Critical for meeting real-time constraints in direct RF sampling pipelines
• Enabled by compilers like TensorRT and Vitis AI

A methodology that incorporates the target accelerator's constraints—memory bandwidth, TOPS/Watt budget, and supported bit-widths—directly into the neural architecture search loop.

• Produces models natively optimized for Edge TPU or NVIDIA Jetson
• Avoids the accuracy cliffs common when forcing generic models onto fixed silicon
• Essential for achieving sub-millisecond inference latency on FPGA-synthesized signal classifiers