AnalogML is here,
and we are helping
to shape its future.

Analog Ml

Traditional machine learning operates on digital data, that is data translated from continuous real-world signals into zeros and ones.

Aspinity’s technology detects and classifies sensor-driven events from raw analog sensor data, allowing developers to design significantly lower-power always-on edge-processing devices. Aspinity’s AML100 chip reduces always-on system power by 95% or more, enabling a variety of event detection applications.

Aspinity’s clients currently rely on Aspinity to develop end-to-end AnalogML applications. Aspinity wants to give clients more control over their data and the software development process when using Aspinity’s hardware. However, the domain of AnalogML is new to most engineers, and most clients have a significant knowledge gap.

Our MHCI capstone team at Carnegie Mellon University takes on this challenge.

SOlution

Supportive Community
A community-based support and learning environment to create a knowledge space that’s self-sustainable and unique to AnalogML.
Contextual Guidance
Built-in contextual walkthroughs, component callouts, and detailed documentation to help onboard new users and traverse the AnalogML knowledge gap.
Intuitive Collaboration
Flexible sharing, commenting and viewing permission controls with the client’s internal team and with the Aspinity team. Astrl supports project privacy while being conducive to collaboration.
View Solution
01. RESEARCH
Stakeholder Mapping Activity
Understanding the problem space through research
02. define
Aspinity Office Visit
Synthesizing research insights
Research Synth Session
Narrowing down our problem space and target audience

People

The primary target users for Astrl are ML Engineers at Aspinity's client companies, who would be tasked to program an AnalogML application to be run on Aspinity's chip. So, someone like Robin.

Robin would interact with co-workers like Cam to understand the analog signals they'll be working with, to effectively extract features out of the data. Robin would also interface with co-workers like Dani to make sure their model runs with the same performance on the actual hardware chip.

03. Ideate
Ideation Session
Ideating solutions to tackle identified problems
04. ProToTYPE
Build prototypes for testing
Bringing ideas to life by building
05. EVALUATE
Receiving User Feedback
Employing user testing to validate our hypothesis
06. REFINE
Refining the prototype and iterating!
Explore Our Process