A Business / Market Opportunity Assessment for Hardware Concepts & Device Designs for Brain-Machine Interfacing

硬件概念的商业/市场机会评估

• 批准号: 571002-2022
• 负责人: Moshovos, Andreas
• 金额: $ 1.08万
• 依托单位: University of Toronto
• 依托单位国家: 加拿大
• 项目类别: Idea to Innovation
• 财政年份: 2021
• 资助国家: 加拿大
• 起止时间: 2021-01-01 至 2022-12-31
• 项目状态: 已结题
• 来源: https://www.nserc-crsng.gc.ca/ase-oro/Details-Detailles_eng.asp?id=728642
• 关键词: Business; Market; Opportunity; Assessment; Hardware

Through advances in brain sensing technology and in neuroscience we are witnessing the emergence of applications that process brain activity data with applications in drug discovery (e.g., decyphering what an animal feels after administering a potential drug) and therapeutics (e.g., recovery or treatment, articles outlining recent developments have even made it into main stream news "Tapping into the Brain to Help a Paralyzed Man to Speak" NY Times and "Brain Implant Gives Blind Woman Artificial Vision in Scientific First" ScienceAlert). In most cases, the data sensed from the brain are converted to digital signals and processed in the digital domain. Unfortunately the amount of computation and memory bandwidth needed for many of these emerging applications far exceed the capability of modern computing systems. However, through specialization we are showing that it is possible to enable such applications to work in real time and in a form factor that makes truly autonomous operation possible. We are seeking the support needed to perform a market assessment for the potential customers and market for our brain-machine computing system. There are numerous applications such as prosthetics, brain function repair/augmentation, and pharmaceutical drug discovery. However, for such applications to become practical, optimized algorithms and dedicated, autonomous wearable processing hardware are essential to analyze and act upon the data in real-time. While these applications will eventually gain approval for use in humans, there is large potential for more immediate use in animal models in a lab setting. Our group, in close collaboration with fully-committed neuroscientists, has been developing hardwareconcepts and designs for digital processing hardware for brain-machine interfacing (BMI) that make suchapplications practical for the first time. Our group is uniquely positioned to deliver the innovations necessary given our decades long expertise in designing high-performance and energy-efficient digital hardware engines, and our extensive experience in designing machine learning hardware.

通过大脑传感技术和神经科学的进步，我们正在目睹处理大脑活动数据的应用程序的出现，这些应用程序用于药物发现（例如，在施用潜在药物后对动物的感觉进行解码）和治疗（例如，在康复或治疗方面，概述最近进展的文章甚至成为主流新闻“利用大脑帮助瘫痪男子说话”纽约时报和“大脑植入物在科学上首先给盲人妇女人工视觉”科学警报）。在大多数情况下，从大脑感知的数据被转换为数字信号并在数字域中进行处理。不幸的是，这些新兴应用中的许多应用所需的计算量和存储器带宽远远超过了现代计算系统的能力。然而，通过专业化，我们表明，它是可能的，使这样的应用程序工作在真实的时间和形式因素，使真正的自主操作成为可能。我们正在寻求所需的支持，以便对我们的脑机计算系统的潜在客户和市场进行市场评估。 有许多应用，如假肢，脑功能修复/增强和药物发现。然而，为了使这些应用变得实用，优化的算法和专用的自主可穿戴处理硬件对于实时分析和处理数据至关重要。虽然这些应用最终将获得批准用于人类，但在实验室环境中更直接地用于动物模型的潜力很大。 我们的团队与全身心投入的神经科学家密切合作，一直在开发用于脑机接口（BMI）的数字处理硬件的硬件概念和设计，使此类应用首次实用化。我们的团队具有独特的优势，能够提供必要的创新，因为我们在设计高性能和高能效数字硬件引擎方面拥有数十年的专业知识，以及我们在设计机器学习硬件方面的丰富经验。