Beyond von Neumann Era: Brain-inspired Hyperdimensional Computing to the Rescue
Beyond von Neumann Era: Brain-inspired Hyperdimensional Computing to the Rescue
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DOI：
10.1145/3566097.3568354
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发表时间：
2023-01
期刊：
影响因子：
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通讯作者：
H. Amrouch;P. Genssler;M. Imani;Mariam Issa;Xun Jiao;Wegdan Mohammad;Gloria Sepanta;Ruixuan Wang
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文献类型：
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作者：
H. Amrouch;P. Genssler;M. Imani;Mariam Issa;Xun Jiao;Wegdan Mohammad;Gloria Sepanta;Ruixuan Wang
深度学习(DL)的突破不断推动创新，深刻改善我们的日常生活。然而，DNN在处理单元和存储单元之间的海量数据移动压倒了传统的计算体系结构。因此，要改进甚至取代已有数十年历史的冯·诺伊曼体系结构，新颖的计算机体系结构是不可或缺的。然而，远远超出现有的冯·诺依曼原理，对所执行的计算带来了深刻的可靠性挑战。这是由于模拟计算和新兴的Beyond-Cmos技术固有的噪声，不可避免地导致不可靠的计算。因此，新的稳健算法成为超越冯·诺伊曼时代界限的关键。超维计算(HDC)正在迅速崛起，成为传统DL和ML算法的一种有吸引力的替代方案。与传统的DL和ML算法不同，HDC在更高效的硬件实现中对错误具有内在的健壮性。除了在硬件级别的这些优势之外，HDC承诺从很少的数据和底层代数中学习，从而在应用程序级别实现了新的可能性。在这项工作中，讨论了HDC算法的抗错误和超越冯·诺伊曼结构的稳健性。最后，以孤立点检测和强化学习为例，说明了HDC作为机器学习算法的优点。
Breakthroughs in deep learning (DL) continuously fuel innovations that profoundly improve our daily life. However, DNNs overwhelm conventional computing architectures by their massive data movements between processing and memory units. As a result, novel computer architectures are indispensable to improve or even replace the decades-old von Neumann architecture. Nevertheless, going far beyond the existing von Neumann principles comes with profound reliability challenges for the performed computations. This is due to analog computing together with emerging beyond-CMOS technologies being inherently noisy and inevitably leading to unreliable computing. Hence, novel robust algorithms become a key to go beyond the boundaries of the von Neumann era. Hyper-dimensional Computing (HDC) is rapidly emerging as an attractive alternative to traditional DL and ML algorithms. Unlike conventional DL and ML algorithms, HDC is inherently robust against errors along a much more efficient hardware implementation. In addition to these advantages at hardware level, HDC's promise to learn from little data and the underlying algebra enable new possibilities at the application level. In this work, the robustness of HDC algorithms against errors and beyond von Neumann architectures are discussed. Further, the benefits of HDC as a machine learning algorithm are demonstrated with the example of outlier detection and reinforcement learning.