C-LSTM: Enabling Efficient LSTM using Structured Compression Techniques on FPGAs (1803.06305v1)

Abstract: Recently, significant accuracy improvement has been achieved for acoustic recognition systems by increasing the model size of Long Short-Term Memory (LSTM) networks. Unfortunately, the ever-increasing size of LSTM model leads to inefficient designs on FPGAs due to the limited on-chip resources. The previous work proposes to use a pruning based compression technique to reduce the model size and thus speedups the inference on FPGAs. However, the random nature of the pruning technique transforms the dense matrices of the model to highly unstructured sparse ones, which leads to unbalanced computation and irregular memory accesses and thus hurts the overall performance and energy efficiency. In contrast, we propose to use a structured compression technique which could not only reduce the LSTM model size but also eliminate the irregularities of computation and memory accesses. This approach employs block-circulant instead of sparse matrices to compress weight matrices and reduces the storage requirement from $\mathcal{O}(k^2)$ to $\mathcal{O}(k)$. Fast Fourier Transform algorithm is utilized to further accelerate the inference by reducing the computational complexity from $\mathcal{O}(k^2)$ to $\mathcal{O}(k\text{log}k)$. The datapath and activation functions are quantized as 16-bit to improve the resource utilization. More importantly, we propose a comprehensive framework called C-LSTM to automatically optimize and implement a wide range of LSTM variants on FPGAs. According to the experimental results, C-LSTM achieves up to 18.8X and 33.5X gains for performance and energy efficiency compared with the state-of-the-art LSTM implementation under the same experimental setup, and the accuracy degradation is very small.

• The paper introduces structured compression using block-circulant matrices to reduce LSTM model complexity on FPGAs.
• It leverages FFT to cut computational complexity from O(k²) to O(k log k), achieving up to 18.8X performance improvements.
• The research enables efficient deployment of large LSTM models on resource-constrained hardware, improving energy efficiency by 33.5X.

An Evaluation of C-LSTM: Enhancing the Efficiency of LSTM Models on FPGAs Using Structured Compression

This paper, titled "C-LSTM: Enabling Efficient LSTM using Structured Compression Techniques on FPGAs," presents a comprehensive paper centered on addressing the growing computational demands required by LSTM network models, especially as their size continues to expand to meet higher accuracy requirements for acoustic recognition systems. The research is fundamental in its proposition that structured compression, particularly the utilization of block-circulant matrices, offers substantial benefits over traditional unstructured pruning-based approaches, leading to efficient designs on FPGA hardware while maintaining minimal accuracy degradation.

Research Methodology and Results

The methodology revolves around the structured compression of LSTM model weight matrices using block-circulant matrices. Unlike unstructured pruning, where matrices become irregular and computational challenges arise due to sparsity, structured compression effectively reduces storage requirements from $\mathcal{O}(k^2)$ to $\mathcal{O}(k)$ by transforming dense matrices to block-circulant forms. The Fast Fourier Transform (FFT) algorithm is then applied to accelerate matrix computations, significantly reducing computational complexity from $\mathcal{O}(k^2)$ to $\mathcal{O}(k\log k)$—a central achievement of the paper.

Empirically, the results from experimental evaluations demonstrate that the newly proposed compressive C-LSTM framework achieves up to 18.8X performance gain and 33.5X enhancement in energy efficiency compared to existing FPGA implementations, notably the state-of-the-art ESE framework. The model compression maintained accuracy with minimal degradation, underscoring the balance struck between computational efficiency and predictive precision.

Practical and Theoretical Implications

Practically, the implications of this research lie in its ability to facilitate the deployment of larger, more complex LSTM models on resource-constrained platforms like FPGAs. This makes high-accuracy LSTM applications more viable without compromising speed and energy consumption—a critical factor in applications such as real-time acoustic recognition systems where both performance and power efficiency are paramount.

Theoretically, the structured integration and adaptation of compression techniques for recurrent neural networks open avenues to explore similar advancements in other machine learning models, where data dependencies and computational complexity are significant deployment barriers. Moreover, the paper proposes a model for future research into refining FPGA synthesis processes for neural network deployment, potentially spurring advancements in high-level synthesis techniques and optimization frameworks applicable to a broader range of artificial intelligence models.

Future Research Directions

The promising results suggest several future research directions. One path involves exploring the application of structured compression to other architectures such as GRUs or hybrid recurrent models to understand its versatility and limitations. Additionally, there's an opportunity to explore the automated optimization frameworks for FPGAs, potentially enhancing its capabilities to accommodate varying neural network models with unique dependencies.

This paper makes clear strides within the technical community, offering robust insights into the optimization and efficient deployment of LSTMs on FPGAs. It sets the foundation for future explorations into harmonizing model compression techniques with hardware-specific executions, ultimately translating to broader AI deployment on edge devices where performance constraints are as critical as model accuracy.