Automatic heterogeneous quantization of deep neural networks for low-latency inference on the edge for particle detectors (2006.10159v3)

Abstract: Although the quest for more accurate solutions is pushing deep learning research towards larger and more complex algorithms, edge devices demand efficient inference and therefore reduction in model size, latency and energy consumption. One technique to limit model size is quantization, which implies using fewer bits to represent weights and biases. Such an approach usually results in a decline in performance. Here, we introduce a method for designing optimally heterogeneously quantized versions of deep neural network models for minimum-energy, high-accuracy, nanosecond inference and fully automated deployment on chip. With a per-layer, per-parameter type automatic quantization procedure, sampling from a wide range of quantizers, model energy consumption and size are minimized while high accuracy is maintained. This is crucial for the event selection procedure in proton-proton collisions at the CERN Large Hadron Collider, where resources are strictly limited and a latency of ${\mathcal O}(1)~\mu$s is required. Nanosecond inference and a resource consumption reduced by a factor of 50 when implemented on field-programmable gate array hardware are achieved.

• The paper introduces an automatic heterogeneous quantization method that optimizes DNNs for low-latency edge inference in particle detectors.
• It employs per-layer quantization with variable bit-widths to cut hardware resource usage by up to 50x while maintaining accuracy.
• The approach is validated on FPGA deployments via hls4ml, ensuring real-time inference with minimal power and area overhead.

Overview of Heterogeneous Quantization for Edge Inference

The paper "Automatic heterogeneous quantization of deep neural networks for low-latency inference on the edge for particle detectors" presents a method for optimizing the quantization of deep neural networks (DNNs) to meet stringent resource constraints while maintaining high accuracy. This is particularly significant for applications like event selection in particle detectors at CERN’s Large Hadron Collider (LHC), where extreme data rates and limited computational resources necessitate efficient, low-latency inference.

Key Contributions and Methodology

The authors introduce several key advancements:

1. Quantization Techniques: Implementation of various quantization methods in a scalable library, allowing for easy sampling and application across model layers and parameters. These methods are part of the broader AutoQKeras and QKeras toolkits, which seamlessly integrate with TensorFlow Keras models.
2. Heterogeneous Quantization: The research highlights an automatic approach to achieving optimal quantization, where bit-widths and numerical representations vary per layer and parameter type. This is crucial for reducing hardware resource consumption like power and area while preserving model accuracy.
3. Reduced Footprint with High Accuracy: By using per-layer heterogeneous quantization, the models achieve significant reductions in resource utilization—a 50-fold decrease in certain cases—without substantial loss in accuracy. This enables inference within nanoseconds, a critical requirement at the LHC for efficient event filtering.
4. Deployment on FPGA: The optimized models are translated into highly parallel firmware for Field-Programmable Gate Array (FPGA) implementation through the hls4ml tool. This ensures that quantized models maintain their performance characteristics once deployed on chip.

Experimental Evaluation and Results

An experiment conducted on classification tasks with data from proton-proton collisions demonstrates:

• Model Efficiency: A comparison between various models shows that optimally quantized models reduce DSP utilization (from 56% to about 1%) and LUT usage significantly, while still performing competitively in terms of classification accuracy.
• Inference Latency: The approach achieves low-latency inference suitable for real-time applications in high-energy physics, highlighting the practical feasibility of deploying such optimized DNNs in FPGA hardware environments.

Implications and Future Developments

The implications of this research are substantial, notably for environments with extreme area and power constraints. The methodology allows for adaptation of complex models to fit within the bounds of available hardware resources, an essential feature for edge computing applications beyond particle physics, such as mobile devices and autonomous vehicles.

Looking forward, future developments might focus on refining the energy consumption estimates to more closely align with specific hardware architectures and exploring integrations with other quantization libraries to enhance versatility. Such advancements could further expand the application of this technology across various domains, emphasizing its relevance in modern AI infrastructure.

This paper contributes significantly to the field by bridging the gap between highly accurate DNN models and their deployment in limited-resource environments, thereby enhancing the viability of AI applications in exceedingly constrained settings.