608 research outputs found
Approximate Computing Survey, Part II: Application-Specific & Architectural Approximation Techniques and Applications
The challenging deployment of compute-intensive applications from domains
such Artificial Intelligence (AI) and Digital Signal Processing (DSP), forces
the community of computing systems to explore new design approaches.
Approximate Computing appears as an emerging solution, allowing to tune the
quality of results in the design of a system in order to improve the energy
efficiency and/or performance. This radical paradigm shift has attracted
interest from both academia and industry, resulting in significant research on
approximation techniques and methodologies at different design layers (from
system down to integrated circuits). Motivated by the wide appeal of
Approximate Computing over the last 10 years, we conduct a two-part survey to
cover key aspects (e.g., terminology and applications) and review the
state-of-the art approximation techniques from all layers of the traditional
computing stack. In Part II of our survey, we classify and present the
technical details of application-specific and architectural approximation
techniques, which both target the design of resource-efficient
processors/accelerators & systems. Moreover, we present a detailed analysis of
the application spectrum of Approximate Computing and discuss open challenges
and future directions.Comment: Under Review at ACM Computing Survey
Mantis: Enabling Energy-Efficient Autonomous Mobile Agents with Spiking Neural Networks
Autonomous mobile agents such as unmanned aerial vehicles (UAVs) and mobile
robots have shown huge potential for improving human productivity. These mobile
agents require low power/energy consumption to have a long lifespan since they
are usually powered by batteries. These agents also need to adapt to
changing/dynamic environments, especially when deployed in far or dangerous
locations, thus requiring efficient online learning capabilities. These
requirements can be fulfilled by employing Spiking Neural Networks (SNNs) since
SNNs offer low power/energy consumption due to sparse computations and
efficient online learning due to bio-inspired learning mechanisms. However, a
methodology is still required to employ appropriate SNN models on autonomous
mobile agents. Towards this, we propose a Mantis methodology to systematically
employ SNNs on autonomous mobile agents to enable energy-efficient processing
and adaptive capabilities in dynamic environments. The key ideas of our Mantis
include the optimization of SNN operations, the employment of a bio-plausible
online learning mechanism, and the SNN model selection. The experimental
results demonstrate that our methodology maintains high accuracy with a
significantly smaller memory footprint and energy consumption (i.e., 3.32x
memory reduction and 2.9x energy saving for an SNN model with 8-bit weights)
compared to the baseline network with 32-bit weights. In this manner, our
Mantis enables the employment of SNNs for resource- and energy-constrained
mobile agents.Comment: To appear at the 2023 International Conference on Automation,
Robotics and Applications (ICARA), February 2023, Abu Dhabi, UAE. arXiv admin
note: text overlap with arXiv:2206.0865
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