In the realm of deep learning, data and computation are paramount. Whoever possesses greater data or faster computing power tends to gain an edge. Consequently, GPUs, which excel in general-purpose basic computing and offer rapid processing speeds, have swiftly become the go-to choice for AI-driven computations.
At NVIDIA's 2017 GTC conference, the company unveiled its latest GPU innovation, the Volta. At the core of this chip lies the TensorCore, an AI accelerator that serves as the hardware foundation for the next wave of AI applications. To fully harness the capabilities of this accelerator, both software upgrades and algorithmic advancements are necessary. First, the current AI algorithms fail to maximize the performance of this accelerator, and second, such enhancements are crucial for achieving further breakthroughs in AI development.
If we can leverage this new generation of chips effectively, not only will it accelerate the progression of AI applications, but it could also lead to the creation of entirely new AI applications. For instance, AI algorithms might capitalize on the chip’s high-speed processing to better interpret and analyze human language. Speech recognition systems could see marked improvements, with transcriptions becoming more precise, and computers developing language systems capable of expressing style and emotion.
Several companies have acknowledged AI's vast potential, investing in the development of powerful chips to facilitate broader AI adoption. NVIDIA's GPUs and Google's TPUs stand out as prime examples.
These chips share a common trait: they continuously refine algorithms based on the principle of program locality. To achieve this local advantage, both AI chips and AI algorithms must work harmoniously. Currently, emerging AI chips provide a foundational framework for this (such as Volta's "TensorCore"), but many AI algorithms haven't yet been adapted to fully utilize these chips' capabilities. In simpler terms, the existing algorithms don’t fully tap into the high-speed computational prowess of these chips.
The initial phase of AI chips focuses on parallel processing, enabling multiple tasks to run simultaneously. Training large neural networks on extensive datasets highlights the significant parallelism that existing parallel chips can exploit. However, memory extraction performance remains far from meeting expectations. Ultimately, these new chips may encounter the “memory wall†issue, where memory performance significantly constrains the chip’s overall performance.
To move beyond this, AI chips must focus more on locality—repeatedly referencing the same variables. For instance, if you're shopping and have a list of ten items, asking ten friends to locate one item each might seem efficient, but it's inefficient if different items are located nearby, causing friends to search similar aisles redundantly. A better approach is to assign each friend to a different aisle, focusing solely on finding items within that aisle—a strategy to address the "memory wall."
New AI chips require algorithms with pronounced local characteristics. Not all AI algorithms currently possess this capability due to a lack of strong locality. Computer vision algorithms benefit from convolutional neural networks, which inherently exhibit local advantages. However, recurrent neural networks used in language and text applications need modifications, particularly in optimizing reasoning abilities, to enhance their locality.
At Baidu’s Silicon Valley AI Lab, researchers have experimented with various methods to improve algorithms and unlock the potential of locality. Early trials show promising signs of overcoming this challenge. For example, researchers enhanced the RNN network, achieving a 30x speedup for smaller data volumes. This is a promising start, though further chip performance improvements are needed. Another research direction involves integrating concepts from convolutional and recurrent neural networks, but the ideal solution in this area remains elusive.
Deep learning AI algorithms are computationally constrained, and past breakthroughs owe much to faster computing machines. Current algorithms have made strides in speech recognition, machine translation, and voice synthesis. The hardware for the next stage of AI algorithm development is already in place, with early experiments suggesting we’re on the cusp of breakthroughs in next-generation algorithms. These upcoming algorithms are poised to fully exploit the capabilities of contemporary AI chips, paving the way for additional innovations.
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