This month we interviewed Greg Mattes, president and CEO of tech startup Analog Computing Solutions. This Indiana University spinout has been developing and refining its Extended Analog Computer, which has practical applications for use with hearing aids and prosthetics.
Q: The technology behind the Extended Analog Computer is quite complex. Can you please describe this technology in layman’s terms?
A: The Extended Analog Computer (EAC) is a radical departure from digital signal processing (DSP), deriving its computational power through a low-power, high-speed analog data path combined with the flexibility of digital configuration. A traditional signal processing paradigm converts a complex analog signal to digital values, performs mathematical manipulations and then converts back to an analog waveform. The EAC, with its analog data path, is able to perform these operations in a single, near instantaneous step.
Q: Can you talk about some of the potential markets for this technology?
A: The EAC is well suited for a variety of signal processing applications, including hearing aids and advanced myoelectric prosthetics. Current hearing aid research focuses on developing advanced sound filtering algorithms. Incremental improvements in the speed and power consumption of hearing aid DSPs are unable to meet the computing requirements of these advanced filtering techniques. The EAC, with its low-power, high-speed architecture, holds the potential to realize these advanced algorithms in hearing aids, resulting in improved hearing and battery life.
New myoelectric interface techniques, such as targeted muscle reinnervation (TMR), are simplifying the use of advanced prosthetics by amputees. However, the dramatic increase in the number and density of electrode sites has increased the signal processing requirements beyond the power and speed capabilities of mobile DSP. EACs, with their low-power, high-speed architecture, promise to surmount these challenges, improving functionality of prosthetics and the quality of life for amputees.
Q: Who are your competitors and what sets you apart? If you don’t have a direct competitor, what technology would your product displace?
A: The competition is the traditional digital signal processing devices that are currently being used in each of these applications. In challenging signal processing applications, this hardware sometimes falls short in either processing time or power required. The high-speed and low-power analog data path of the EAC – combined with the flexibility of digital configuration – promises to surmount these challenges.
Q: Where are you currently in the development process?
A: The computing architecture of the EAC has been validated through simulations and hardware prototypes. Current development focuses on using machine learning techniques to configure networks of EACs for signal classification. We hope to have this stage of development complete in early 2015.
Q: Where do you stand on funding today, and what are your plans for raising capital over the next 12 months?
A: This development is funded by an investment from Indiana University angel investors through the Building Entrepreneurs in Software & Technology business plan competition. We are also funded through an STTR Phase I grant from the National Science Foundation. In the next 12 months, we plan to raise capital through additional grants from the NSF, but may consider outside investments depending on the direction of the technology and company.
Q: What is the timeline for getting the product ready for market?
A: Our initial market will likely be in advanced prosthetics. After we configure networks of EACs to perform complex signal classification and quantify the benefit of the EAC over the competition, we plan to pursue licensing deals with prosthetics manufacturers mid-2015.