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

本文介绍了一个117级100 Hz ~ 10 kHz的硅耳蜗，其动态范围为61 dB，功耗为0.5mW。这个人工耳蜗的动态范围是迄今为止所有人工耳蜗中最宽的。宽动态范围是在一个低噪声的行波结构与分布式增益控制。我们对电子耳蜗的分析揭示了为什么大自然更喜欢行波机制而不是一大堆带通滤波器来分解声音。我们建议在3个项目中使用硅耳蜗，这些项目的时间跨度为4- 5年。第一个项目，耳蜗增益控制的基本问题将扩展和扩展我们以前在耳蜗增益控制方面的工作，以进一步提高其性能，将有助于理解增益控制在生物耳蜗中的功能作用，并将产生对耳蜗植入语音处理器有用的计算原语和电路，以及鲁棒和自适应语音和语音模式识别。我们希望这个项目能够通过CAREER提案获得资助。第二个项目，聋人耳蜗植入语音处理器将专注于使用硅耳蜗作为耳蜗植入语音处理器的低功耗前端。我们希望这些处理器的性能能够显著提高，因为硅耳蜗模拟了生物耳蜗所表现出的几种重要效应，而目前的植入物没有，并且还因为它可以以低功耗真实的时间实现这些计算密集型生物算法。耳蜗植入项目将与马萨诸塞州眼耳医院的唐纳德·爱丁顿博士合作完成，他是耳蜗植入领域的先驱，他非常高兴与我们合作。我们希望利用CAREER基金来帮助启动这个项目，但主要是通过NIH或NSF的其他赠款来资助。第三个项目，语音和模式识别的自适应前端将专注于使用基于尖峰的混合（模拟-数字）计算技术，该技术是作者在贝尔实验室发明的，以构建一个节能和自适应网格矢量量化器，语音预处理器的重要组成部分。网格矢量quantizeris有用的量化序列的模拟矢量与一定的时间dependenciesbetween他们之间，如那些出现从耳蜗;我们计划实施onchiplearning，以适应这个量化器的参数;这样的quantizermaps架构自然的混合状态机（HSM）的架构，一个机器thextends和扩展的概念，一个有限状态机的混合域。我们希望从朗讯科技或其他资助机构获得资金，以补充该项目的开始资金。我们将与贝尔实验室和麻省理工学院的语音研究人员合作，我们建议在麻省理工学院开设两门课程，名为电子学和生物学中的反馈和混合计算，为期3-4年，将介绍电子学，神经示例和自适应和混合计算的概念。

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.