The Growing Demand and Market Drivers for
Always‐on Sensing and Listening
In the next five years, billions of handsfree, always‐on sensing devices that run on battery will assist us in our daily lives at home and at work: playing music on request, controlling our home’s temperature and lights, alerting us to danger, monitoring the wear and tear of factory equipment, even continuously monitoring our health.
These devices are always‐on, continuously digitizing and analyzing all sensor data as they wait to detect a random sporadic event, such as a voice, an alarm, a slight variation in the vibrational frequency of an
engine or a change in heart rhythm. This constant analysis of mostly irrelevant data is grossly inefficient―expending precious system resources on data that will ultimately be thrown away.
Aspinity’s Reconfigurable Analog Modular Processor (RAMP) technology enables a fundamentally new, more efficient edge architecture that does just that. RAMP technology incorporates powerful machine learning (ML) into an ultra‐low‐power analog neuromorphic processor that detects unique events from background noise using the raw analog sensor data, before the data is digitized.
TinyML Makes a Huge Impact on Mobile Devices
At last month’s tinyML Summit, Aspinity joined other industry thought leaders, including Qualcomm, Samsung, Google, and Arm, to explore the size and power challenges of using tinyML to extract intelligence from the physical world. Eliminating the bottlenecks in developing a power- and size-optimized neural network through more efficient algebraic functions and memory access was a big part of the conversation. There was also a collective buzz on establishing industry benchmarks for comparing the energy efficiency of the digital cores that comprise today’s tinyML chips.
For more information: https://www.aspinity.com/
Contact: Thomas Doyle, CEO - firstname.lastname@example.org