TY - GEN
T1 - Invited
T2 - 61st ACM/IEEE Design Automation Conference, DAC 2024
AU - Gomony, Manil Dev
AU - Ahn, Bas
AU - Luiken, Rick
AU - Biyani, Yashvardhan
AU - Gebregiorgis, Anteneh
AU - Laborieux, Axel
AU - Zenke, Friedemann
AU - Hamdioui, Said
AU - Corporaal, Henk
PY - 2024
Y1 - 2024
N2 - Artificial Intelligence (AI) supported by Deep Artificial Neural Networks (ANNs) is booming and already used in many applications, with impressive results, and we are still its infancy. For many sensing applications it would be advantageous if we could move AI from cloud to Edge. However this requires huge improvements in energy-efficiency. The CONVOLVE project (convolve.eu) aims at enabling smart edge devices through a concerted effort at all layers of the design stack. This ranges from using much more efficient models and mappings, like exploiting Spiking Neural Networks (SNNs), to new processing architectures, like compute-in-memory (CIM), use of approximation, and using new device technology, like memristors. However these latter changes make HW more susceptible to noise and other disturbances. Online continuous learning (i.e. adapting weights) may alleviate these problems. This paper shows several CONVOLVE developments in the crucial areas of CIM architectures, SNN accelerators and online learning.
AB - Artificial Intelligence (AI) supported by Deep Artificial Neural Networks (ANNs) is booming and already used in many applications, with impressive results, and we are still its infancy. For many sensing applications it would be advantageous if we could move AI from cloud to Edge. However this requires huge improvements in energy-efficiency. The CONVOLVE project (convolve.eu) aims at enabling smart edge devices through a concerted effort at all layers of the design stack. This ranges from using much more efficient models and mappings, like exploiting Spiking Neural Networks (SNNs), to new processing architectures, like compute-in-memory (CIM), use of approximation, and using new device technology, like memristors. However these latter changes make HW more susceptible to noise and other disturbances. Online continuous learning (i.e. adapting weights) may alleviate these problems. This paper shows several CONVOLVE developments in the crucial areas of CIM architectures, SNN accelerators and online learning.
UR - http://www.scopus.com/inward/record.url?scp=85211119497&partnerID=8YFLogxK
U2 - 10.1145/3649329.3689623
DO - 10.1145/3649329.3689623
M3 - Conference contribution
AN - SCOPUS:85211119497
T3 - Proceedings - Design Automation Conference
BT - Proceedings of the 61st ACM/IEEE Design Automation Conference, DAC 2024
PB - IEEE
Y2 - 23 June 2024 through 27 June 2024
ER -