以人为中心的计算——超维度方法的案例

J. Rabaey, Abbas Rahimi, Sohum Datta, M. Rusch, P. Kanerva, B. Olshausen
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引用次数: 2

摘要

物联网和蜂群概念的一些最引人注目的应用领域涉及人类如何与周围的世界以及网络世界进行交互。虽然通信和数据处理设备的激增深刻地改变了我们的互动模式,但我们处理输入(感觉)和输出(驱动)的方式几乎没有改变。物联网(swarm)和可穿戴设备的结合提供了改变这一切的潜力,为真正的人类增强打开了大门。它的一个缩影就是直接连接到人类大脑。然而,要理解从通常嘈杂的传感器接收到的大量信息,并在非常严格的延迟范围(< 10毫秒)内做出可靠的决策,通常需要在可穿戴设备中以极高的能效执行巨大的计算工作负载。在本次演讲中,我们将说明为什么替代的非冯·诺伊曼计算范式和架构可能是这些认知处理任务的正确选择。更重要的是,我们将关注一种称为超维计算(HDC)的计算模型,并用具体的例子来说明为什么这种方法在后摩尔数据驱动的领域可能是正确的。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
Human-centric computing — The case for a Hyper-Dimensional approach
Some of most compelling application domains of the IoT and Swarm concepts relate to how humans interact with the world around it and the cyberworld beyond. While the proliferation of communication and data processing devices has profoundly altered our interaction patterns, little has been changed in the way we process inputs (sensory) and outputs (actuation). The combination of IoT (Swarms) and wearable devices offers the potential for changing all of this, opening the door for true human augmentation. The epitome of this would be a direct interface to the human brain. Yet, making sense of the plethora of information received from the often noisy sensors and making reliable decisions within very tight latency bounds (< 10 ms) typically demands huge computational workloads to be performed in wearable form factors at extreme energy efficiency. In this presentation, we will make the case why alternative non-Von Neumann computational paradigms and architectures may be the right choice for these cognitive processing tasks. Even more, we will focus on a computational model called Hyper-Dimensional Computing (HDC), and illustrate with concrete examples of why this approach may be the right one in a post-Moore data-driven arena.
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