Background

The enormous impact from the growth and expansion of the internet-of-things (IoT) can be seen across industries and applications. From smart home applications, to health care, industrial automation, smart cities and agriculture, defense and consumer electronics, IoT has led to improved functionality, efficiency and convenience. As we continue to grow more reliant on IoT networks, and as the demands we place on those networks continue to increase, one of the major challenges that will need to be addressed is power efficiency. It will be critical to solve the challenge of minimizing power consumption and increasing the lifetime of batteries. . Ideally, IoT devices should be seamlessly deployed with little need for maintenance, or monitoring and replacement of batteries. Additionally, IoT edge-devices must address the modern computing needs of machine-learning and deep-learning to have a dedicated computing infrastructure for even higher power gains.

Technology Overview

Researchers at Northeastern University are addressing the energy consumption limitations of IoT devices. One of their core technologies involves a novel Wake-Up Radio (WUR) design which detects the incoming RF signal independent of the interference of the channel. The power of the received signal is detected with a novel design of the Receiver Signal Strength Indicator (RSSI). Based on the power of the incoming signal, this RSSI circuit generates a unique code and subsequent processing can be based on the code received. The power of the received signal maps to a code which improves the Bit Error Rate (BER) of the RF receiver. The entire code of the Wake-Up Radio can be received with extremely low power consumption (few nWs). These designs can be applied to IoT devices in order to ensure secure, seamless communication with little to no power cost.

This team of researchers has also designed an ultra-low power subthreshold Gm stage where transconductance is very stable with respect to process, temperature, and voltage variations. This completely changes the way analog circuits are used in an integrated circuit, and will help cut down testing costs, reduce area, and enable ultra-low-power analog computing. Furthermore, they have developed an analog computing infrastructure which significantly reduces the size and power consumption of a machine learning ASIC and lowers energy output/energy requirements for devices.

Finally, the team is also in the process of expanding their pipeline with a beam forming wireless power transfer (WPT) scheme. They aim to maximize the received RF energy in a three-pronged way: An ultra-low power (ULP) closed loop, beamforming, energy transfer scheme to maximize the received RF energy; a high efficiency RF rectifier circuit to enhance RF-to-DC conversion efficiency; and a high efficiency power management unit to operate the rectifier at its maximum power point. This WPT scheme could increase power harvesting by several orders of magnitude.

Benefits

Extremely low power consumption
Significantly increases operational lifetime
Removes need for battery replacement, reducing cost and weight of IoT devices
High noise resilience
Reduced BER
Faster Wake-Up

Applications

IoT devices

Opportunity

Testing and validating MVP with pilot partner(s)
Applying for grant funding

Ultra-Low Power Wake-Up Radio Architecture

IP Status

Seeking