Career:The Adaptive Silicon Cochlea: Biology, VLSI, and Applications

职业：自适应硅耳蜗：生物学、VLSI 和应用

• 批准号: 9984451
• 负责人: Rahul Sarpeshkar
• 金额: $ 20万
• 依托单位: Massachusetts Institute of Technology
• 依托单位国家: 美国
• 项目类别: Standard Grant
• 财政年份: 2000
• 资助国家: 美国
• 起止时间: 2000-04-15 至 2005-03-31
• 项目状态: 已结题
• 来源: https://www.nsf.gov/awardsearch/showAward?AWD_ID=9984451&HistoricalAwards=false
• 关键词: Career; Adaptive; Silicon; Cochlea; Biology

We described a 117-stage 100Hz-to-10kHz silicon cochlea that attained a dynamic rangeof 61dB while dissipating 0.5mW of power. This cochlea has the widest dynamic rangeof any artificial cochlea built to date. The wide dynamic range was attained in a low-noise traveling-wave architecture with distributed gain control. The analysis of ourelectronic cochlea suggested why nature preferred a traveling-wave mechanism over abank-of bandpass filters to decompose sounds.We propose to use the silicon cochlea in 3 projects that will range over a span of 4-5years. The first project, Fundamental Issues in Cochlear Gain Control will extend andexpand our previous work on gain control in the cochlea to improve its performancefurther, will help understand the functional role of gain control in the biological cochleabetter, and will yield computational primitives and circuits that are useful for cochlear-implant speech processors, and for robust-and-adaptive speech and auditory-patternrecognition. We expect this project to be funded soley through the CAREER proposal.The second project, Cochlear-Implant Speech Processors for the Deaf will focuss onusing the silicon cochlea as a low-power front end in cochlear-implant speech processors.We expect the performance of these processors to significantly improve because thesilicon cochlea models several important effects that are exhibited by the biologicalcochlea that current implants do not, and also because it can implement thesecomputationally-intensive biological algorithms in real time and with low power. Thecochlear-implant project will be done in collaboration with Dr. Donald Eddington at theMassachusetts Eye and Ear Infirmary, who is a pioneer in the field of cochlear implantsand who is very happy to collaborate with us. We expect to use CAREER funding to helpus get started on this project, but fund it primarily via other grants from the NIH or NSF.The third project, Adaptive Front Ends for Speech and Pattern Recognition will focuss onusing spike-based hybrid (analog-digital) computation techniques that were invented bythe author at Bell Labs to construct an energy-efficient and adaptive trellis vectorquantizer, an important component of speech preprocessors. The trellis vector quantizeris useful in quantizing sequences of analog vectors with certain temporal dependenciesbetween them, such as those that emerge from a cochlea; we plan to implement onchiplearning to adapt the parameters of this quantizer; the architecture of such a quantizermaps naturally to the architecture of a hybrid state machine (HSM), a machine thatextends and expands the concept of a finite state machine to the hybrid domain. Weexpect to supplement the beginning funding for this project from the CAREER proposalwith funding from Lucent Technologies or other funding agencies. We will collaboratewith speech researchers at Bell Labs and at MIT on this project.We propose to develop two courses at MIT, entitled Feedback in Electronics andBiology, and Hybrid Computation over a span of 3-4 years that will introduce intoelectronics, neural examples and concepts in adaptive-and-hybrid computing.

我们描述了一个117级的100赫兹到10千赫的硅耳蜗，它的动态范围达到了61dB，同时消耗了0.5 mW的功率。这是迄今为止建造的所有人工耳蜗中最宽的动态范围。在分布式增益控制的低噪声行波结构中获得了较宽的动态范围。对电子耳蜗的分析表明，为什么大自然更喜欢行波机制而不是带通滤光片来分解声音。我们建议在3个项目中使用硅耳蜗，时间跨度为4-5年。第一个项目，耳蜗增益控制的基本问题将进一步扩展和发展我们之前在耳蜗增益控制方面的工作，以提高其性能，将有助于理解增益控制在生物耳蜗机中的功能作用，并将产生对人工耳蜗式语音处理器、稳健和自适应语音和听觉模式识别有用的计算原语和电路。我们预计这个项目将通过职业计划书单独获得资金。第二个项目，面向聋人的人工耳蜗式语音处理器将专注于在人工耳蜗式语音处理器中使用硅耳蜗片作为低功耗的前端。我们预计这些处理器的性能将显著提高，因为硅耳蜗片模拟了生物耳蜗片所表现出的几个当前植入物所没有的重要影响，还因为它可以实时且低功耗地实现计算密集型生物算法。人工耳蜗项目将与马萨诸塞州眼耳医院的唐纳德·艾丁顿博士合作完成，他是人工耳蜗领域的先驱，非常乐意与我们合作。我们希望利用事业资金帮助开始这个项目，但主要通过NIH或NSF的其他拨款来资助它。第三个项目，语音和模式识别的自适应前端将专注于使用贝尔实验室作者发明的基于尖峰的混合(模拟-数字)计算技术来构建节能和自适应的网格矢量量化器，这是语音预处理器的重要组成部分。格状矢量量化器在量化具有一定时间相关性的模拟向量序列时很有用，例如从耳蜗处出现的那些；我们计划实现一个学习来适应这个量化器的参数；这样的量化器的体系结构自然地映射到混合状态机(HSM)的体系结构，该机器将有限状态机的概念延伸和扩展到混合域。我们希望从职业计划书中补充这个项目的开始资金，并由朗讯技术公司或其他资助机构提供资金。我们将与贝尔实验室和麻省理工学院的语音研究人员在这个项目上合作。我们建议在麻省理工学院开发两门课程，名为电子学和生物学中的反馈，以及混合计算，在3-4年的时间里，介绍电子学、神经例子和自适应和混合计算的概念。