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This paper presents the new approach in implementation of analog-to-digital converter (ADC) that is based on Hopfield neural-network architecture. Hopfield neural ADC (NADC) is a type of recurrent neural network that is effective in solving simple optimization problems, such as analog-to-digital conversion. The main idea behind the proposed design is to use multiple 2-bit Hopfield NADCs operating as quantizers in parallel, where analog input signal to each successive 2-bit Hopfield ADC block is passed through a voltage level shifter. This is followed by a neural network encoder to remove the quantization errors. In traditional Hopfield NADC based designs, increasing the number of bits could require proper scaling of the network parameters, in particular digital output operating region. Furthermore, the resolution improvement of traditional Hopfield NADC creates digital error that increases with the increasing number of bits. The proposed design is scalable in number of bits and number of quantization levels, and can maintain the magnitude of digital output code within a manageable operating voltage range.
The inherent stochasticity in many nano-scale devices makes them prospective candidates for low-power computations. Such devices have been demonstrated to exhibit probabilistic switching between two stable states to achieve stochastic behavior. Recen
High-speed high-resolution Analog-to-Digital Conversion is the key part for waveform digitization in physics experiments and many other domains. This paper presents a new fully digital correction of mismatch errors among the channels in Time Interlea
Ferroelectric field effect transistors (FeFETs) are being actively investigated with the potential for in-memory computing (IMC) over other non-volatile memories (NVMs). Content Addressable Memories (CAMs) are a form of IMC that performs parallel sea
We study and analyze the fundamental aspects of noise propagation in recurrent as well as deep, multi-layer networks. The main focus of our study are neural networks in analogue hardware, yet the methodology provides insight for networks in general.
Time to Digital Converters (TDCs) are very common devices in particles physics experiments. A lot of off-the-shelf TDCs can be employed but the necessity of a custom DAta acQuisition (DAQ) system makes the TDCs implemented on the Field-Programmable G