Compressing deep neural networks on FPGAs to binary and ternary precision with HLS4ML (2003.06308v2)

Abstract: We present the implementation of binary and ternary neural networks in the hls4ml library, designed to automatically convert deep neural network models to digital circuits with FPGA firmware. Starting from benchmark models trained with floating point precision, we investigate different strategies to reduce the network's resource consumption by reducing the numerical precision of the network parameters to binary or ternary. We discuss the trade-off between model accuracy and resource consumption. In addition, we show how to balance between latency and accuracy by retaining full precision on a selected subset of network components. As an example, we consider two multiclass classification tasks: handwritten digit recognition with the MNIST data set and jet identification with simulated proton-proton collisions at the CERN Large Hadron Collider. The binary and ternary implementation has similar performance to the higher precision implementation while using drastically fewer FPGA resources.

Compressing Deep Neural Networks on FPGAs to Binary and Ternary Precision with hls4ml

This paper presents an intricate paper of employing binary and ternary precision in deep neural network (DNN) models on field-programmable gate arrays (FPGAs). The focus is the integration of these quantization strategies into the {\tt hls4ml} library, which facilitates automatic conversion of DNN models into FPGA firmware, optimizing for resource efficiency while maintaining competitive performance.

Main Contributions

The researchers address the challenge of limited computational resources on FPGAs by investigating binary and ternary precision for DNN deployment. These networks drastically reduce the resource consumption by representing network parameters with one or two bits, with binary weights adopting values of +1 or -1 and ternary weights including an additional 0.

Several strategies are explored to develop binary and ternary models from existing full-precision models. The approaches vary from directly replacing precision levels in existing architectures to expanding network sizes to bridge accuracy gaps caused by precision losses. The primary benchmark examples utilized to validate these methods are handwritten digit recognition with the MNIST dataset and jet identification at the CERN Large Hadron Collider (LHC).

Results and Observations

Significant findings of the paper include:

• Performance Preservation: Binary and ternary networks successfully maintain performance metrics relatively close to the baseline floating-point precision (FPP) models. The models demonstrate minimal degradation in accuracy—often matching or even exceeding expectations given the reduction in resource usage.
• Resource Efficiency: The binary networks notably achieve zero DSP utilization for certain layers, a critical factor for environments limited by DSP availability. Ternary networks similarly optimize resource use but offer superior performance compared to binary networks at a modest increase in resource demand.
• Latency and Throughput: The paper reports impressively low latencies in the order of hundreds of nanoseconds, making binary and ternary networks suitable for real-time applications, such as those in high-energy physics scenarios like the LHC.

Implications and Future Directions

The paper underscores the practical relevance of utilizing binary and ternary precision in low-latency environments. This research provides insights into reducing FPGA resource utilization while sustaining computational speed, thereby extending the applicability of DNNs in time-critical applications.

Looking forward, there is a direction to enhance the flexibility of integrating complex models with different levels of precision, potentially mixed within the same network. This approach could cater to specific requirements of various application domains beyond high-energy physics. The potential to combine binary/ternary networks with pruning and other model compression techniques offers another promising pathway for resource-constrained environments.

In summary, this work efficiently aligns the purpose of deploying DNNs in FPGAs with the constraints of precision and resource availability, fostering advancements in real-time processing systems.