Issues And Challenges In Super-Resolution Object Detection And Recognition


If you want high performance AI inference, such as Super-Resolution Object Detection and Recognition, in your SoC the challenge is to find a solution that can meet your needs and constraints. You need inference IP that can run the model you want at high accuracy. You need inference IP that can run the model at the frame rate you want: higher frame rate = lower latency, more time for dec... » read more

Overview of Machine Learning Algorithms Used In Hardware Security (TU Delft)


A new technical paper titled "A Survey on Machine Learning in Hardware Security" was published by researchers at TU Delft. Abstract "Hardware security is currently a very influential domain, where each year countless works are published concerning attacks against hardware and countermeasures. A significant number of them use machine learning, which is proven to be very effective in ... » read more

Feasibility of Using Domain Wall-Magnetic Tunnel Junction for Magnetic Analog Addressable Memories


A new technical paper titled "Domain Wall-Magnetic Tunnel Junction Analog Content Addressable Memory Using Current and Projected Data" was published by researchers at UT Austin and Samsung Advanced Institute of Technology (SAIT). Abstract "With the rise in in-memory computing architectures to reduce the compute-memory bottleneck, a new bottleneck is present between analog and digital conver... » read more

Solving The Reliability Problem Of Memristor-Based Artificial Neural Networks


A technical paper titled "ReMeCo: Reliable Memristor-Based in-Memory Neuromorphic Computation" was published by researchers at Eindhoven University of Technology, University of Tehran, and USC. Abstract: "Memristor-based in-memory neuromorphic computing systems promise a highly efficient implementation of vector-matrix multiplications, commonly used in artificial neural networks (ANNs). H... » read more

Co-Design View of Cross-Bar Based Compute-In-Memory


A new review paper titled "Compute in-Memory with Non-Volatile Elements for Neural Networks: A Review from a Co-Design Perspective" was published by researchers at Argonne National Lab, Purdue University, and Indian Institute of Technology Madras. "With an over-arching co-design viewpoint, this review assesses the use of cross-bar based CIM for neural networks, connecting the material proper... » read more

Energy-Efficient Execution Scheme For Dynamic Neural Networks on Heterogeneous MPSoCs


A technical paper titled "Map-and-Conquer: Energy-Efficient Mapping of Dynamic Neural Nets onto Heterogeneous MPSoCs" was published (preprint) by researchers at LAMIH/UMR CNRS, Universite Polytechnique Hauts-de-France and UC Irvine. Abstract "Heterogeneous MPSoCs comprise diverse processing units of varying compute capabilities. To date, the mapping strategies of neural networks (NNs) onto ... » read more

Review of Methods to Design Secure Memristor Computing Systems


A technical paper titled "Review of security techniques for memristor computing systems" was published by researchers at Israel Institute of Technology, Friedrich Schiller University Jena (Germany), and Leibniz Institute of Photonic Technology (IPHT). Abstract "Neural network (NN) algorithms have become the dominant tool in visual object recognition, natural language processing, and robotic... » read more

Simulating Reality: The Importance Of Synthetic Data In AI/ML Systems For Radar Applications


Artificial intelligence and machine learning (AI/ML) are driving the development of next-generation radar perception. However, these AI/ML-based perception models require enough data to learn patterns and relationships to make accurate predictions on new, unseen data and scenarios. In the field of radar applications, the data used to train these models is often collected from real-world meas... » read more

Research Bits: Jan. 24


Transistor-free compute-in-memory Researchers from the University of Pennsylvania, Sandia National Laboratories, and Brookhaven National Laboratory propose a transistor-free compute-in-memory (CIM) architecture to overcome memory bottlenecks and reduce power consumption in AI workloads. "Even when used in a compute-in-memory architecture, transistors compromise the access time of data," sai... » read more

Research Bits: Jan. 17


Ionic circuit for neural nets Researchers at Harvard University and DNA Script developed an ionic circuit comprising hundreds of ionic transistors for neural net computing. While ions in water move slower than electrons in semiconductors, the team noted that the diversity of ionic species with different physical and chemical properties could be harnessed for more diverse information process... » read more

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