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Aspinity Machine Learning Chip Extends Battery Life for Portable IoT Devices

SEMI spoke with Tom Doyle, founder and CEO of Aspinity, about the challenges of packing more localized intelligence into portable Internet of Things (IoT) devices without draining their batteries. Doyle shared his views on Aspinity’s system-level approach – solve the power problems by performing machine learning in analog – ahead of his presentation at the SEMI MEMS & Imaging Sensors Technology Showcase, 18 February, as part of the SEMI Technology Unites Global Summit, 15-19 February 2021, online event.

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SEMI: Why is power efficiency so important for IoT devices?

Doyle: Hundreds of millions of IoT devices are improving our lives at home and at work. Always on and always sensing the environment for data, these smart devices have traditionally been wall-powered and have relied on the cloud for their data processing needs, but clogged networks, as well as privacy and performance issues, have necessitated the migration to edge processing.

Aspinity logoSpanning consumer, medical and industrial, these IoT devices are becoming smaller and more portable. And a portion of them is operating remotely in hard-to-access locations. So now we are packing more functionality into the device and we are moving to battery power and the batteries need to last a long time. That is a big challenge before us, and to answer it, we need to find the most power-efficient ways to integrate always-on sensing capability into IoT devices because we cannot afford to have short battery life limit market adoption.

SEMI: Why is it so challenging to deliver low-power, always-on solutions and how can sensors suppliers achieve improvements in system power?

Doyle: In today’s always-on IoT devices, all sensor data – which are naturally analog – is immediately digitized at high resolution, and then it’s analyzed to determine whether a wake word has been spoken, a specific motion has been made, or some other anomaly has occurred. But since most of the data collected will not contain the information for which the device is waiting, this digitize-first approach wastes significant battery life by continuously running irrelevant data through the ADC and the digital processor.

Aspinity TUGS logoSensors suppliers have some options to consider for reducing power. If they are satisfied achieving incremental improvements in battery life, both sensors and digital processor suppliers can continue to drive down the power of each individual component in the system. But to achieve revolutionary power savings, we must look at a more holistic system solution.

The fundamental problem is that moving data through a system costs power. That is why the most efficient way to save power is to reduce the amount of data down to what’s actually important as early as possible, right at the start of the signal chain, where the physical world becomes data. If we can minimize the amount of data that require downstream processing, then we can maximize battery life.

SEMI: Aspinity aims to solve the battery-life problem in IoT devices by introducing a new system architecture. Could you explain how your approach differs from digitize-first?

Doyle: Aspinity’s solution, called the Reconfigurable Analog Modular Processor (RAMP), is an analog processing technology that combines analog machine learning (analogML™) and analog compression to enable accurate, ultra-low-power analog event detection and system wake-up. RAMP technology enables a new system architecture, which we call analyze-first, that allows an always-on system to spend just a little bit of analog power up front at the sensor to determine whether sensed data are relevant to the task at hand before waking the digital system for further processing. The analyze-first architecture can extend battery life by months or years over digitize-first architectures because it keeps the higher-power digital components asleep unless important data require digitization and analysis, which in some applications – such as voice-first or acoustic event detection – may occur very rarely.

 

SEMI: Can you give us an example?

Doyle: Here is a practical example of how this works: For most voice-enabled systems, such as smart speakers, voice-activated TV remotes and hearables, voice is only present 10%-20% of the time – but the digitize-first architecture on which these devices are traditionally based is digitizing 100% of the sound data captured by the microphone, even when most of that data are irrelevant and could not possibly contain a wake word.

In contrast, the RAMP-based analyze-first architecture is highly efficient since it uses feature extraction and a neural network to analyze the sound at the microphone, right where it enters the device, to determine if the sound contains voice before waking the digital wake word engine. Additionally, the accuracy of most wake word engines relies not just on waking up and analyzing the wake word, but also on analyzing the 500ms of sound prior to the wake word (preroll). To support wake word engine performance, the RAMP also continuously compresses 500ms of preroll that can be stored in just 2k of memory and delivered to the wake word engine along with the voice data. So, this new analyze-first approach using RAMP technology can extend battery life by 10 times over older digitize-first designs, without sacrificing performance and accuracy.

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SEMI: What solutions can Aspinity bring to address the current market needs?

Doyle: Aspinity offers the only analogML chip for always-on IoT devices that run on battery: the RAMP chip.

The RAMP is trainable and programmable to detect many different types of sensor events directly from the raw analog sensor data. One application that benefits from a RAMP chip are devices that are always-listening for voice, for glass break or alarms, or for some other type of sound. Other examples include vibration sensors that monitor industrial equipment for predictive and preventative maintenance, and heartrate sensors that are used to detect anomalies in wearables and other biomedical applications.

Affinity PQAspinity just recently introduced our voice-first evaluation kit – which we will be demonstrating during the Technology Showcase at Technology Unites – to enable our customers to get first-hand experience with our RAMP-based analog voice wake-up solution. With this complete hardware and software kit, customers can experience all of the benefits of analogML and analog data compression – 10x power savings without a reduction in wake word detection accuracy –for their next generation of voice-enabled devices.

SEMI: How can technology unite us? What do you expect from your participation at SEMI Technology Unites Global Summit?

Doyle: I think this past year has shown us that when time gets tough – and for many of us, the COVID-19 pandemic has been one of the most difficult challenges we have faced – that innovation is critical to solving major problems. The microelectronics industry has played an important role in providing critical components for COVID-19 testing, ventilators, air-purification systems, and other equipment used in healthcare settings. COVID-19 has also accelerated the move to voice as a preferred interface to many devices in an effort to stem the spread of germs on surfaces.

The biotech industry is gearing up to provide the vaccines that we hope will restore more normalcy to our daily lives. We can thank the successful collaborations between R&D innovators and established companies in many different markets for the new devices and drugs now going into production.

With traditional in-person conferences still on hold until the pandemic eases up, attending industry conferences with exceptional speakers presenting interesting content is more important than ever. SEMI Technology Unites Global Summit provides that opportunity, and I’m genuinely looking forward to participating.

Aspinity Tom DoyleTom Doyle, Founder and CEO of Aspinity, brings over 30 years of experience in operational excellence and executive leadership in analog and mixed-signal semiconductor technology to Aspinity. Prior to Aspinity, Tom was group director of Cadence Design Systems’ analog and mixed-signal IC business unit, where he managed the deployment of the company’s technology to the world’s foremost semiconductor companies. Previously, Tom was founder and president of the analog/mixed-signal software firm, Paragon IC solutions, where he was responsible for all operational facets of the company including sales and marketing, global partners/distributors, and engineering teams in the US and Asia. Tom holds a B.S. in Electrical Engineering from West Virginia University and an MBA from California State University, Long Beach.

Serena Brischetto is senior manager of Marketing and Communications at SEMI Europe.

Topics: MEMS , sensors , microelectronics , IoT , Internet of Things , machine learning , microelectronics industry , semiconductors , imaging sensors , Aspinity , smart devices , battery life , RAMP , portable IoT devices , analogML , Reconfigurable Analog Modular Processor , voice-enabled systems , Technology Unites Global Summit , analyze-first architecture , SEMI MEMS & Imaging Sensors Technology Showcase , always-on solutions , power savings

 

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