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Found 12 papers, 2 papers with code
H3DFact: Heterogeneous 3D Integrated CIM for Factorization with Holographic Perceptual Representations
Disentangling attributes of various sensory signals is central to human-like perception and reasoning and a critical task for higher-order cognitive and neuro-symbolic AI systems.
Algorithm-Hardware Co-Design of Distribution-Aware Logarithmic-Posit Encodings for Efficient DNN Inference
Traditional Deep Neural Network (DNN) quantization methods using integer, fixed-point, or floating-point data types struggle to capture diverse DNN parameter distributions at low precision, and often require large silicon overhead and intensive quantization-aware training.
Towards Cognitive AI Systems: a Survey and Prospective on Neuro-Symbolic AI
The remarkable advancements in artificial intelligence (AI), primarily driven by deep neural networks, have significantly impacted various aspects of our lives.
Scaling Compute Is Not All You Need for Adversarial Robustness
Finally, we make our benchmarking framework (built on top of \texttt{timm}~\citep{rw2019timm}) publicly available to facilitate future analysis in efficient robust deep learning.
Real-Time Fully Unsupervised Domain Adaptation for Lane Detection in Autonomous Driving
While deep neural networks are being utilized heavily for autonomous driving, they need to be adapted to new unseen environmental conditions for which they were not trained.
FRL-FI: Transient Fault Analysis for Federated Reinforcement Learning-Based Navigation Systems
Swarm intelligence is being increasingly deployed in autonomous systems, such as drones and unmanned vehicles.
iELAS: An ELAS-Based Energy-Efficient Accelerator for Real-Time Stereo Matching on FPGA Platform
Stereo matching is a critical task for robot navigation and autonomous vehicles, providing the depth estimation of surroundings.
An Energy-Efficient Quad-Camera Visual System for Autonomous Machines on FPGA Platform
In our past few years' of commercial deployment experiences, we identify localization as a critical task in autonomous machine applications, and a great acceleration target.
AutoPilot: Automating SoC Design Space Exploration for SWaP Constrained Autonomous UAVs
Balancing a computing system for a UAV requires considering both the cyber (e. g., sensor rate, compute performance) and physical (e. g., payload weight) characteristics that affect overall performance.
A Survey of FPGA-Based Robotic Computing
On the other hand, FPGA-based robotic accelerators are becoming increasingly competitive alternatives, especially in latency-critical and power-limited scenarios.
QuaRL: Quantization for Fast and Environmentally Sustainable Reinforcement Learning
We believe that this is the first of many future works on enabling computationally energy-efficient and sustainable reinforcement learning.
AdaptivFloat: A Floating-point based Data Type for Resilient Deep Learning Inference
Conventional hardware-friendly quantization methods, such as fixed-point or integer, tend to perform poorly at very low word sizes as their shrinking dynamic ranges cannot adequately capture the wide data distributions commonly seen in sequence transduction models.
