Related papers: Deep Analog-to-Digital Converter for Wireless Comm…
In recent years, Compute-in-memory (CiM) architectures have emerged as a promising solution for deep neural network (NN) accelerators. Multiply-accumulate~(MAC) is considered a {\textit de facto} unit operation in NNs. By leveraging the…
In this article, deep learning is applied to estimate the uplink channels for mixed analog-to-digital converters (ADCs) massive multiple-input multiple-output (MIMO) systems, where a portion of antennas are equipped with high-resolution…
In task-based quantization, a multivariate analog signal is transformed into a digital signal using a limited number of low-resolution analog-to-digital converters (ADCs). This process aims to minimize a fidelity criterion, which is…
A novel background calibration technique for Time-Interleaved Analog-to-Digital Converters (TI-ADCs) is presented in this paper. This technique is applicable to equalized digital communication receivers. As shown by Tsai et al. [1] and Luna…
Employing low-resolution analog-to-digital converters (ADCs) for millimeter wave receivers with large antenna arrays provides opportunity to efficiently reduce power consumption of the receiver. Reducing ADC resolution, however, results in…
Network-on-Chip (NoC) is currently the paradigm of choice to interconnect the different components of System-on-Chips (SoCs) or Chip Multiprocessors (CMPs). As the levels of integration continue to grow, however, current NoCs face…
In this work, the problem of signal parameter estimation from measurements acquired by a low-complexity analog-to-digital converter (ADC) with $1$-bit output resolution and an unknown quantization threshold is considered. Single-comparator…
As the bridge between the analog world and digital computers, analog-to-digital converters are generally used in modern information systems such as radar, surveillance, and communications. For the configuration of analog-to-digital…
For 5G it will be important to leverage the available millimeter wave spectrum. To achieve an approximately omni- directional coverage with a similar effective antenna aperture compared to state of the art cellular systems, an antenna array…
Modern communication receiver architectures center around digital signal processing (DSP), with the bulk of the receiver processing being performed on digital signals obtained after analog-to-digital conversion (ADC). In this paper, we…
This paper develops a new deep neural network optimized equalization framework for massive multiple input multiple output orthogonal frequency division multiplexing (MIMOOFDM) systems that employ low-resolution analog-to-digital converters…
Analog in-memory computing (AIMC) is an energy-efficient alternative to digital architectures for accelerating machine learning and signal processing workloads. However, its energy efficiency is limited by the high energy cost of the column…
Analog-to-digital converters (ADCs) facilitate the conversion of analog signals into a digital format. While the specific designs and settings of ADCs can vary depending on their applications, it is crucial in many modern applications to…
This work discusses memory-immersed collaborative digitization among compute-in-memory (CiM) arrays to minimize the area overheads of a conventional analog-to-digital converter (ADC) for deep learning inference. Thereby, using the proposed…
This paper considers uplink massive MIMO systems with 1-bit analog-to-digital converters (ADCs) and develops a deep-learning based channel estimation framework. In this framework, the prior channel estimation observations and deep neural…
Deep neural networks are widely deployed in many fields. Due to the in-situ computation (known as processing in memory) capacity of the Resistive Random Access Memory (ReRAM) crossbar, ReRAM-based accelerator shows potential in accelerating…
We study collaborative machine learning systems where a massive dataset is distributed across independent workers which compute their local gradient estimates based on their own datasets. Workers send their estimates through a multipath…
The practical deployment of massive multiple-input multiple-output (MIMO) in future fifth generation (5G) wireless communication systems is challenging due to its high hardware cost and power consumption. One promising solution to address…
Wireless systems with inband full-duplex transceiver typically require multiple lines of defense against the effect of harsh self-interference, specifically, to avoid saturation of the analog-to-digital converter (ADC) in the receiver. We…
Low resolution analog-to-digital converters (ADCs) can be employed to improve the energy efficiency (EE) of a wireless receiver since the power consumption of each ADC is exponentially related to its sampling resolution and the hardware…