AI/ML enables understanding & awareness of sensor data, but existing “dumb” sensors offload data w/ enormous energy cost for communication. 

Local compute eliminates communication, but compute energy limits local AI/ML, reducing device lifetime and performance.

Efficient enables ubiquitous AI/ML. Our patented chip architecture provides energy-minimal intelligence at the extreme edge. Efficient devices are future-proof because they are general-purpose and programmable in software post-deployment, and maintenance-free because they consume 1/100th of the energy of existing technology. An Efficient chip can last for five to ten years on a small battery.

Programmable, energy-minimal computer architectures. Graham Gobieski. PhD thesis 2022.

RipTide: A programmable, energy-minimal dataflow compiler and architecture. Graham Gobieski, Souradip Ghosh, Marijn Heule, Todd Mowry, Tony Nowatzki, Nathan Beckmann, Brandon Lucia. MICRO 2022.

SNAFU: An Ultra-Low-Power, Energy-Minimal CGRA-Generation Framework and Architecture. Graham Gobieski, Oguz Atli, Ken Mai, Brandon Lucia, Nathan Beckmann. ISCA 2021.

MANIC: An Energy-Efficient Architecture for Ultra-Low-Power Embedded Systems. Graham Gobieski, Amolak Nagi, Nathan Serafin, Mehmet Meric Isgenc, Nathan Beckmann, Brandon Lucia. MICRO 2019.

Intelligence Beyond the Edge: Inference on Intermittent Embedded Systems. Graham Gobieski, Brandon Lucia, Nathan Beckmann. ASPLOS 2019.

Intermittent Deep Neural Network Inference. Graham Gobieski, Nathan Beckmann, Brandon Lucia. SysML 2018.