Aspinity, a semiconductor startup funded by Alexa Fund and others, and based in Pittsburgh, USA, recently announced the first smart-sensing edge architecture to tackle the power- and data-efficiency problems in the billions of battery-powered consumer electronics, smart home systems, and predictive-maintenance devices on which we increasingly rely.
Aspinity announced its reconfigurable analog modular processor (RAMP) platform, an ultra-low power, analog processing platform, that overcomes the power and data handling challenges in battery-operated, always-on sensing devices.
Incorporating machine learning into an analog neuromorphic processor, Aspinity’s RAMP platform enables 10x power savings over older architectures. Devices can now run for months or years, instead of days or weeks, without battery recharge or replacement.
Smart-sensing edge architecture
Elaborating on Aspinity’s smart-sensing edge architecture, Tom Doyle, CEO and founder, said that Aspinity offers a fundamentally new architectural approach to conserving power and data resources in always-on devices. The scalable and programmable RAMP technology incorporates powerful machine learning into an ultra-low power analog neuromorphic processor that can detect unique events from background noise before the data is digitized.
By directly analyzing the analog raw sensor data for what’s important, the RAMP chip eliminates the higher-power processing of irrelevant data.
System designers can now stop sacrificing features and accuracy for longer battery life. Aspinity’s analyze-first approach reduces the power consumption of always-sensing systems by up to 10x and data requirements by up to 100x.
The RAMP chip’s analog blocks can be reprogrammed with application-specific algorithms for detection of different events and different types of sensor input. For example, designers can use a RAMP chip for always-listening applications, where the chip conserves system power by keeping the rest of the always-listening system in a low power sleep state, until a specific sound, such as voice or an alarm, has been detected.
Unlike the other sensor edge solutions for voice activity detection, the RAMP chip also supports voice-first devices by storing the pre-roll data required by wake word engines.
For industrial applications, designers can use a RAMP chip to sample and select only the most important data points from thousands of points of sensor data: compressing vibration data into a reduced number of frequency/energy pairs and dramatically decreasing the amount of data collected and transmitted for analysis. This is the USP for the RAMP platform.
With so many ways to program a RAMP core, as well as broad algorithm support for different types of analysis and output, the RAMP chip uniquely enables a whole new generation of smaller, lower-cost, more power- and data-efficient, battery-operated, always-on devices for consumer, IoT, industrial and biomedical applications.
Much longer battery life
Short battery life makes always-on sensing devices unattractive. Will this change? Aspinity certainly thinks so! Doyle said manufacturers of battery-powered always-on sensing devices certainly want to improve battery life because users don’t want to have to frequently recharge or replace batteries.
Is this going to cut into battery suppliers’ revenues? He added that Aspinity does not remove the need to have a battery. Rather, it improves the battery life considerably, saving up to 10X battery in always-on sensing devices and up to 100x data. Thus, battery suppliers will still sell batteries; their batteries will last longer on a single charge (for devices that have rechargeable batteries). Device manufacturers may also be able to move to smaller batteries.
Demand for always-on sensing devices surging
Why is the demand for always-on sensing devices surging? For example, there are folks who may not appreciate this. Doyle noted: “People enjoy interacting with electronic devices in an untethered way (voice-first) – and they want this to feel natural. So, they want those devices listening all the time. They also want to have smart home devices that are wirelessly connected – and they want to have industrial machinery connected to wireless sensor nodes as well. This is the user interface migration story.
Finally, how does RAMP incorporate modular, parallel and continuously operating analog blocks that mimic the brain’s efficiency? Also, wasn’t this tried earlier? The patented and innovative RAMP technology enables sophisticated digital signal processing tasks to be replicated in analog.
Aspinity has leveraged the nonlinear characteristics of a small number of transistors to enable a new architectural approach to machine learning: modular, parallel and continuously operating analog blocks mimic the brain’s efficient neural network.
These blocks are configurable for typical analog tasks such as sensor interfacing, signal processing and data conversion as well as more complex tasks such as feature extraction, event detection and classification. Each one of these blocks is implemented in a much smaller footprint than a traditional analog block and allows early event detection from raw, unstructured analog sensor data.