Approximate Computing: Concepts, Architectures, Challenges, Applications, and Future Directions
AM Dalloo, AJ Humaidi, AK Al Mhdawi… - IEEE …, 2024 - ieeexplore.ieee.org
The unprecedented progress in computational technologies led to a substantial proliferation
of artificial intelligence applications, notably in the era of big data and IoT devices. In the …
of artificial intelligence applications, notably in the era of big data and IoT devices. In the …
EnforceSNN: Enabling resilient and energy-efficient spiking neural network inference considering approximate DRAMs for embedded systems
Spiking Neural Networks (SNNs) have shown capabilities of achieving high accuracy under
unsupervised settings and low operational power/energy due to their bio-plausible …
unsupervised settings and low operational power/energy due to their bio-plausible …
Approximate Memory with Protected Static Allocation
J Fabrício Filho, I Felzmann… - 2022 IEEE 34th …, 2022 - ieeexplore.ieee.org
Approximate memories provide energy savings or performance improvements at the cost of
occasional errors in stored data. Applications that tolerate errors on their data profit from this …
occasional errors in stored data. Applications that tolerate errors on their data profit from this …
EnforceSNN: Enabling resilient and energy-effcient spiking neural network inference considering approximate DRAMs for embedded systems.
RV Wicaksana Putra, MA Hanif… - Frontiers in …, 2022 - search.ebscohost.com
Abstract Spiking Neural Networks (SNNs) have shown capabilities of achieving high
accuracy under unsupervised settings and low operational power/energy due to their bio …
accuracy under unsupervised settings and low operational power/energy due to their bio …