The blog post explores the relative speeds of Vision Transformers (ViTs) and Convolutional Neural Networks (CNNs), finding that while ViTs theoretically have lower computational complexity, they are often slower in practice. This discrepancy arises from optimized CNN implementations benefiting from decades of research and hardware acceleration. Specifically, highly optimized convolution operations, efficient memory access patterns, and specialized hardware like GPUs favor CNNs. While ViTs can be faster for very high-resolution images where their quadratic complexity is less impactful, they generally lag behind CNNs at common image sizes. The author concludes that focused optimization efforts are needed for ViTs to realize their theoretical speed advantages.
The blog post "The Speed of VITs and CNNs" by Lucas Beyer delves into a detailed comparison of the computational efficiency of Vision Transformers (ViTs) and Convolutional Neural Networks (CNNs), challenging the common perception that ViTs are inherently slower. The author meticulously examines the factors influencing inference speed, dissecting the computational graph of both architectures and highlighting the nuances often overlooked in simplistic comparisons.
Beyer begins by acknowledging the prevalent belief in the slower speed of ViTs, often attributed to the quadratic complexity of self-attention with respect to the input sequence length. However, he argues that focusing solely on this aspect provides an incomplete picture. He emphasizes the importance of considering other factors, including the patch size, the number of tokens processed, and the embedded dimension, all of which significantly impact the overall computational cost. Furthermore, he underscores the role of hardware optimizations and implementation details, which can significantly skew performance benchmarks.
The post proceeds to systematically analyze the computational complexity of various operations within both ViTs and CNNs. It breaks down the cost of self-attention in ViTs, relating it to the number of patches and the embedding dimension. Simultaneously, it analyzes the complexity of convolutions in CNNs, considering factors like kernel size, stride, and the number of input and output channels. Through this detailed analysis, Beyer demonstrates that the computational cost of self-attention can be comparable, or even less, than the cost of convolutions in certain scenarios, especially when dealing with smaller image sizes and fewer tokens.
The author then delves into the practical aspects of measuring inference speed, explaining the importance of controlling for variables such as batch size, hardware platform, and software optimizations. He points out that using different libraries, compilers, and hardware accelerators can significantly impact performance comparisons, making it crucial to ensure a fair and consistent evaluation methodology. Furthermore, the post highlights the significance of memory access patterns and caching effects, which can substantially influence the actual execution time of both ViTs and CNNs.
Beyer reinforces his arguments with experimental results, presenting benchmark data on various hardware platforms, including CPUs and GPUs. He showcases scenarios where ViTs achieve comparable or even superior inference speeds compared to CNNs, particularly for smaller input sizes. He also acknowledges the situations where CNNs hold a performance advantage, typically when processing larger images, emphasizing that the optimal choice of architecture depends heavily on the specific application and constraints.
Concluding, the post refutes the oversimplified notion of ViTs being inherently slower than CNNs. It meticulously dissects the computational landscape of both architectures, highlighting the complex interplay of various factors that influence performance. By focusing on a holistic analysis encompassing theoretical complexity, implementation details, and experimental results, Beyer provides a nuanced understanding of the relative speeds of ViTs and CNNs, urging readers to move beyond superficial comparisons and consider the broader context when evaluating these powerful architectures.
Summary of Comments ( 0 )
https://news.ycombinator.com/item?id=43866329
The Hacker News comments discuss the surprising finding in the linked article that Vision Transformers (ViTs) can be faster than Convolutional Neural Networks (CNNs) under certain hardware and implementation conditions. Several commenters point out the importance of efficient implementations and hardware acceleration for ViTs, with some arguing that the article's conclusions might not hold true with further optimization of CNN implementations. Others highlight the article's focus on inference speed, noting that training speed is also a crucial factor. The discussion also touches on the complexities of performance benchmarking, with different hardware and software stacks yielding potentially different results, and the limitations of focusing solely on FLOPs as a measure of efficiency. Some users express skepticism about the long-term viability of ViTs given their memory bandwidth requirements.
The Hacker News post titled "The Speed of VITs and CNNs," linking to an article exploring the speed differences between Vision Transformers (ViTs) and Convolutional Neural Networks (CNNs), generated several comments. Many of the commenters engaged with the nuances of the original article's findings.
One commenter highlighted the importance of considering both inference speed and training speed when comparing model architectures. They pointed out that while CNNs might be faster for inference in certain scenarios, ViTs could potentially train faster, especially with larger datasets. This commenter also mentioned how hardware advancements, particularly related to attention mechanisms, could shift the speed advantage in the future.
Another commenter delved deeper into the hardware aspects, explaining how the memory access patterns of ViTs, characterized by global access, are less efficient on current hardware compared to the localized access patterns of CNNs. This difference in memory access contributes significantly to the speed disparity. They also mentioned the impact of optimized libraries and hardware acceleration specifically designed for CNNs, further widening the performance gap in favor of CNNs on existing hardware.
Further discussion revolved around the complexities of performance measurement. One commenter noted the difficulty in establishing a truly "apples-to-apples" comparison between ViTs and CNNs due to variations in implementations, hyperparameter tuning, and the specific hardware used for benchmarking. They suggested that the benchmarks presented in the article, while informative, should be interpreted with caution, acknowledging the numerous factors that could influence the results.
The trade-off between accuracy and speed was also a recurring theme. Commenters acknowledged that while ViTs have shown impressive accuracy in some tasks, the speed advantage of CNNs, especially for real-time applications, remains a significant factor. This led to a discussion about the potential for future optimizations and architectural modifications to bridge the performance gap and make ViTs more competitive in speed-critical scenarios.
Finally, some comments touched upon the broader context of model selection in machine learning. The choice between ViTs and CNNs, as pointed out by one commenter, depends heavily on the specific application and its requirements. While CNNs might be preferred for applications demanding low latency, ViTs could be more suitable for tasks where accuracy is paramount, even at the cost of slower processing.