Emerging AI chips bring breakthroughs to algorithms

In the realm of deep learning, data and computational power stand as the two cornerstones. Those who possess greater data volumes or superior computational capabilities often enjoy a competitive edge. Consequently, GPUs, which excel in general-purpose computation and offer rapid processing speeds, have swiftly become the dominant choice for AI-driven computing. At the 2017 GTC Technology Conference, NVIDIA unveiled their latest GPU innovation, Volta. At the core of this chip lies the TensorCore, an AI accelerator designed to pave the way for future AI applications. However, to fully harness the capabilities of this accelerator, both software updates and algorithmic advancements are necessary. Firstly, existing AI algorithms struggle to fully exploit the performance of this hardware. Secondly, achieving another leap forward in AI development requires adapting our algorithms to this new technology. If we can maximize the potential of this cutting-edge hardware, it will not only accelerate the progress of current AI applications but also potentially spawn entirely new ones. For instance, AI algorithms could leverage the chip's high-speed processing to better comprehend and analyze human language. Speech recognition systems would see significant improvements, with transcriptions becoming more precise. Computers might even develop speech systems capable of conveying linguistic nuances and emotions. Recognizing the immense potential of AI, many companies are investing in the development of powerful chips to facilitate broader AI adoption. NVIDIA’s GPUs and Google’s TPUs are prime examples. These chips share a common trait: they continually refine algorithms based on the principle of program locality. To achieve local optimization, both AI chips and algorithms must work in tandem. While emerging AI chips like Volta’s TensorCore provide a foundational framework, many existing AI algorithms have yet to be updated to take full advantage of this hardware. In simple terms, current algorithms cannot fully capitalize on the high-speed operations these chips offer. The initial phase of AI chips focuses on parallel processing, enabling multiple tasks to run simultaneously. Training extensive neural networks on vast datasets showcases the significant parallelism inherent in these chips. Yet, memory retrieval performance lags far behind expectations. Ultimately, these advanced chips will confront the “memory wall” issue, where memory capabilities severely constrain overall performance. To progress further, AI chips must continue to enhance locality, which refers to repeatedly referencing the same variables. For example, if you’re shopping and have a list of ten items, you could ask ten friends to find one item each. While this approach is parallelized, it is inefficient since items on the list may be grouped together, leading to friends searching neighboring aisles and reducing efficiency. A better strategy would involve assigning each friend to a separate aisle to search systematically. This approach addresses the current “memory wall” challenge. Next-generation AI chips must accommodate algorithms with pronounced locality. Currently, not all AI algorithms exhibit strong locality. Algorithms in computer vision benefit significantly from convolutional neural networks due to their inherent locality. However, recurrent neural networks used in language-related applications require modifications—particularly to enhance their reasoning abilities—to improve locality. Researchers at Baidu’s Silicon Valley AI Lab have explored various methods to optimize algorithms and unlock the potential of locality. Early experiments show promising results, suggesting we are close to overcoming this challenge. For instance, researchers have enhanced RNN networks to achieve a 30-fold speedup in low-volume scenarios. This is a positive beginning, but there is still room for improvement in AI chip performance. Another avenue of exploration involves integrating concepts from convolutional and recurrent neural networks, though finding the optimal solution remains a work in progress. Deep learning algorithms are currently constrained by computational limitations. Breakthroughs so far have largely been fueled by the advent of faster computing machines. Despite these achievements in speech recognition, machine translation, and speech synthesis, the hardware groundwork for the next stage of AI algorithm development is already in place. Early experiments suggest we are on the cusp of developing next-generation algorithms. These algorithms are poised to fully exploit the capabilities of contemporary AI chips, driving us toward additional breakthroughs.

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