Related papers: Realizing In-Memory Baseband Processing for Ultra-…
Deep neural networks (DNNs) have made breakthroughs in various fields including image recognition and language processing. DNNs execute hundreds of millions of multiply-and-accumulate (MAC) operations. To efficiently accelerate such…
This paper presents a novel architecture utilizing a 10T SRAM cell for XNOR-based in-memory computing, aimed at mitigating the extensive routing challenges typically encountered in conventional in-memory computing systems. By integrating a…
High-precision optical frequency measurement is indispensable to modern science and technology, yet conventional spectroscopic techniques struggle to resolve sub-linewidth spectral features. We introduce a unique platform for…
Modern data-intensive applications demand high computation capabilities with strict power constraints. Unfortunately, such applications suffer from a significant waste of both execution cycles and energy in current computing systems due to…
Resistive memory-based reconfigurable systems constructed by CMOS-RRAM integration hold great promise for low energy and high throughput neuromorphic computing. However, most RRAM technologies relying on filamentary switching suffer from…
DRAM-based main memory is used in nearly all computing systems as a major component. One way of overcoming the main memory bottleneck is to move computation near memory, a paradigm known as processing-in-memory (PiM). Recent PiM techniques…
Human beings construct perception of space by integrating sparse observations into massively interconnected synapses and neurons, offering a superior parallelism and efficiency. Replicating this capability in AI finds wide applications in…
The increasing computational demand of Convolutional Neural Networks (CNNs) necessitates energy-efficient acceleration strategies. Compute-in-Memory (CIM) architectures based on Resistive Random Access Memory (RRAM) offer a promising…
Many modern workloads, such as neural networks, databases, and graph processing, are fundamentally memory-bound. For such workloads, the data movement between main memory and CPU cores imposes a significant overhead in terms of both latency…
With power consumption becoming a critical processor design issue, specialized architectures for low power processing are becoming popular. Several studies have shown that neural networks can be used for signal processing and pattern…
Holographic multiple-input multiple-output (MIMO) systems with extremely large apertures enable transformational capabilities for sixth-generation (6G) integrated sensing and communications (ISAC). However, existing non-orthogonal multiple…
To index the increasing volume of data, modern data indexes are typically stored on SSDs and cached in DRAM. However, searching such an index has resulted in significant I/O traffic due to limited access locality and inefficient cache…
Computing-In-Memory (CIM) offers a potential solution to the memory wall issue and can achieve high energy efficiency by minimizing data movement, making it a promising architecture for edge AI devices. Lightweight models like MobileNet and…
The evolution of radio access networks (RANs) toward virtualization and openness creates new opportunities for flexible, cost-effective, and high-performance deployments. Achieving real-time and energy-efficient baseband processing on…
This article introduces an energy and spectral efficient multiple-input multiple-output orthogonal frequency division multiplexing (MIMO-OFDM) transmission scheme designed for the future sixth generation (6G) wireless communication…
High-speed signal processing is essential for maximizing data throughput in emerging communication applications, like multiple-input multiple-output (MIMO) systems and radio-frequency (RF) interference cancellation. However, as these…
Microwave quantum memory promises advanced capabilities for noisy intermediate-scale superconducting quantum computers. Existing approaches to microwave quantum memory lack complete combination of high efficiency, long storage time,…
Quantum random access memory (QRAM) enables efficient classical data access for quantum computers -- a prerequisite for many quantum algorithms to achieve quantum speedup. Despite various proposals, the experimental realization of QRAM…
The Discrete Fourier Transform (DFT) is essential for various applications ranging from signal processing to convolution and polynomial multiplication. The groundbreaking Fast Fourier Transform (FFT) algorithm reduces DFT time complexity…
In this article, we address the timely topic of cellular bistatic simultaneous localization and mapping (SLAM) with specific focus on end-to-end processing solutions, from raw I/Q samples, via channel parameter estimation to user equipment…