FastWave: Accelerating Autoregressive Convolutional Neural Networks on FPGA

Autoregressive convolutional neural networks (CNNs) have been widely exploited for sequence generation tasks such as audio synthesis, language modeling and neural machine translation. WaveNet is a deep autoregressive CNN composed of several stacked layers of dilated convolution that is used for sequence generation. While WaveNet produces state-of-the art audio generation results, the naive inference implementation is quite slow; it takes a few minutes to generate just one second of audio on a high-end GPU. In this work, we develop the first accelerator platform~\textit{FastWave} for autoregressive convolutional neural networks, and address the associated design challenges. We design the Fast-Wavenet inference model in Vivado HLS and perform a wide range of optimizations including fixed-point implementation, array partitioning and pipelining. Our model uses a fully parameterized parallel architecture for fast matrix-vector multiplication that enables per-layer customized latency fine-tuning for further throughput improvement. Our experiments comparatively assess the trade-off between throughput and resource utilization for various optimizations. Our best WaveNet design on the Xilinx XCVU13P FPGA that uses only on-chip memory, achieves 66 faster generation speed compared to CPU implementation and 11 faster generation speed than GPU implementation.Comment: Published as a conference paper at ICCAD 201

A Survey on Hardware Accelerators for Large Language Models

Large Language Models (LLMs) have emerged as powerful tools for natural language processing tasks, revolutionizing the field with their ability to understand and generate human-like text. As the demand for more sophisticated LLMs continues to grow, there is a pressing need to address the computational challenges associated with their scale and complexity. This paper presents a comprehensive survey on hardware accelerators designed to enhance the performance and energy efficiency of Large Language Models. By examining a diverse range of accelerators, including GPUs, FPGAs, and custom-designed architectures, we explore the landscape of hardware solutions tailored to meet the unique computational demands of LLMs. The survey encompasses an in-depth analysis of architecture, performance metrics, and energy efficiency considerations, providing valuable insights for researchers, engineers, and decision-makers aiming to optimize the deployment of LLMs in real-world applications

Non-local Attention Optimized Deep Image Compression

This paper proposes a novel Non-Local Attention Optimized Deep Image Compression (NLAIC) framework, which is built on top of the popular variational auto-encoder (VAE) structure. Our NLAIC framework embeds non-local operations in the encoders and decoders for both image and latent feature probability information (known as hyperprior) to capture both local and global correlations, and apply attention mechanism to generate masks that are used to weigh the features for the image and hyperprior, which implicitly adapt bit allocation for different features based on their importance. Furthermore, both hyperpriors and spatial-channel neighbors of the latent features are used to improve entropy coding. The proposed model outperforms the existing methods on Kodak dataset, including learned (e.g., Balle2019, Balle2018) and conventional (e.g., BPG, JPEG2000, JPEG) image compression methods, for both PSNR and MS-SSIM distortion metrics

The Challenge of Machine Learning in Space Weather Nowcasting and Forecasting

The numerous recent breakthroughs in machine learning (ML) make imperative to carefully ponder how the scientific community can benefit from a technology that, although not necessarily new, is today living its golden age. This Grand Challenge review paper is focused on the present and future role of machine learning in space weather. The purpose is twofold. On one hand, we will discuss previous works that use ML for space weather forecasting, focusing in particular on the few areas that have seen most activity: the forecasting of geomagnetic indices, of relativistic electrons at geosynchronous orbits, of solar flares occurrence, of coronal mass ejection propagation time, and of solar wind speed. On the other hand, this paper serves as a gentle introduction to the field of machine learning tailored to the space weather community and as a pointer to a number of open challenges that we believe the community should undertake in the next decade. The recurring themes throughout the review are the need to shift our forecasting paradigm to a probabilistic approach focused on the reliable assessment of uncertainties, and the combination of physics-based and machine learning approaches, known as gray-box.Comment: under revie

Reconfigurable acceleration of Recurrent Neural Networks

Recurrent Neural Networks (RNNs) have been successful in a wide range of applications involving temporal sequences such as natural language processing, speech recognition and video analysis. However, RNNs often require a significant amount of memory and computational resources. In addition, the recurrent nature and data dependencies in RNN computations can lead to system stall, resulting in low throughput and high latency. This work describes novel parallel hardware architectures for accelerating RNN inference using Field-Programmable Gate Array (FPGA) technology, which considers the data dependencies and high computational costs of RNNs. The first contribution of this thesis is a latency-hiding architecture that utilizes column-wise matrix-vector multiplication instead of the conventional row-wise operation to eliminate data dependencies and improve the throughput of RNN inference designs. This architecture is further enhanced by a configurable checkerboard tiling strategy which allows large dimensions of weight matrices, while supporting element-based parallelism and vector-based parallelism. The presented reconfigurable RNN designs show significant speedup over CPU, GPU, and other FPGA designs. The second contribution of this thesis is a weight reuse approach for large RNN models with weights stored in off-chip memory, running with a batch size of one. A novel blocking-batching strategy is proposed to optimize the throughput of large RNN designs on FPGAs by reusing the RNN weights. Performance analysis is also introduced to enable FPGA designs to achieve the best trade-off between area, power consumption and performance. Promising power efficiency improvement has been achieved in addition to speeding up over CPU and GPU designs. The third contribution of this thesis is a low latency design for RNNs based on a partially-folded hardware architecture. It also introduces a technique that balances initiation interval of multi-layer RNN inferences to increase hardware efficiency and throughput while reducing latency. The approach is evaluated on a variety of applications, including gravitational wave detection and Bayesian RNN-based ECG anomaly detection. To facilitate the use of this approach, we open source an RNN template which enables the generation of low-latency FPGA designs with efficient resource utilization using high-level synthesis tools.Open Acces

Retentive Network: A Successor to Transformer for Large Language Models

In this work, we propose Retentive Network (RetNet) as a foundation architecture for large language models, simultaneously achieving training parallelism, low-cost inference, and good performance. We theoretically derive the connection between recurrence and attention. Then we propose the retention mechanism for sequence modeling, which supports three computation paradigms, i.e., parallel, recurrent, and chunkwise recurrent. Specifically, the parallel representation allows for training parallelism. The recurrent representation enables low-cost $O(1)$ inference, which improves decoding throughput, latency, and GPU memory without sacrificing performance. The chunkwise recurrent representation facilitates efficient long-sequence modeling with linear complexity, where each chunk is encoded parallelly while recurrently summarizing the chunks. Experimental results on language modeling show that RetNet achieves favorable scaling results, parallel training, low-cost deployment, and efficient inference. The intriguing properties make RetNet a strong successor to Transformer for large language models. Code will be available at https://aka.ms/retnet

Transformer-based NMT : modeling, training and implementation

International trade and industrial collaborations enable countries and regions to concentrate their developments on specific industries while making the most of other countries' specializations, which significantly accelerates global development. However, globalization also increases the demand for cross-region communication. Language barriers between many languages worldwide create a challenge for achieving deep collaboration between groups speaking different languages, increasing the need for translation. Language technology, specifically, Machine Translation (MT) holds the promise to enable communication between languages efficiently in real-time with minimal costs. Even though nowadays computers can perform computation in parallel very fast, which provides machine translation users with translations with very low latency, and although the evolution from Statistical Machine Translation (SMT) to Neural Machine Translation (NMT) with the utilization of advanced deep learning algorithms has significantly boosted translation quality, current machine translation algorithms are still far from accurately translating all input. Thus, how to further improve the performance of state-of-the-art NMT algorithm remains a valuable open research question which has received a wide range of attention. In the research presented in this thesis, we first investigate the long-distance relation modeling ability of the state-of-the-art NMT model, the Transformer. We propose to learn source phrase representations and incorporate them into the Transformer translation model, aiming to enhance its ability to capture long-distance dependencies well. Second, though previous work (Bapna et al., 2018) suggests that deep Transformers have difficulty in converging, we empirically find that the convergence of deep Transformers depends on the interaction between the layer normalization and residual connections employed to stabilize its training. We conduct a theoretical study about how to ensure the convergence of Transformers, especially for deep Transformers, and propose to ensure the convergence of deep Transformers by putting the Lipschitz constraint on its parameter initialization. Finally, we investigate how to dynamically determine proper and efficient batch sizes during the training of the Transformer model. We find that the gradient direction gets stabilized with increasing batch size during gradient accumulation. Thus we propose to dynamically adjust batch sizes during training by monitoring the gradient direction change within gradient accumulation, and to achieve a proper and efficient batch size by stopping the gradient accumulation when the gradient direction starts to fluctuate. For our research in this thesis, we also implement our own NMT toolkit, the Neutron implementation of the Transformer and its variants. In addition to providing fundamental features as the basis of our implementations for the approaches presented in this thesis, we support many advanced features from recent cutting-edge research work. Implementations of all our approaches in this thesis are also included and open-sourced in the toolkit. To compare with previous approaches, we mainly conducted our experiments on the data from the WMT 14 English to German (En-De) and English to French (En-Fr) news translation tasks, except when studying the convergence of deep Transformers, where we alternated the WMT 14 En-Fr task with the WMT 15 Czech to English (Cs-En) news translation task to compare with Bapna et al. (2018). The sizes of these datasets vary from medium (the WMT 14 En-De, ~ 4.5M sentence pairs) to very large (the WMT 14 En-Fr, ~ 36M sentence pairs), thus we suggest our approaches help improve the translation quality between popular language pairs which are widely used and have sufficient data.China Scholarship Counci