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Multi-lag cross-correlations (X-Corr) are essential building blocks in radar and communication for range/velocity detection and synchronization. Performing X-corrs necessitates efficient delay and correlation blocks. Traditionally, high bandwidth X-corr is performed using high-speed ADCs followed by digital multiply-and-accumulates (MACs). However, 5–20 TOPS/W X-Corr efficiencies lead to 0.1-1W per cross-correlator, limiting deployability in power-constrained applications. Alternatively, to realize X-corr using prior single-lag analog correlators, wideband analog delays (>10ns delays with 4GHz BW) should be integrated on chip to enable multiple lags. Furthermore, replicating N analog correlators, leads to an impractical chip area. Therefore, practical analog X-Corr requires: (i) high input bandwidths, (ii) long correlation length, N for high signal processing gain (SPG=10log10(N)), (iii) high compute-efficiency (>100 TOPS/W) with compute accuracy compared to digital MACs (>7-bit), (iv) single-shot readout across all N X-corr lags in a compact area. In this work, we leverage a sampling-based approach to create large analog delays and area/power-efficient four-transistor analog compute cell to present a margin-propagation (MP) based fully-analog X-Corr compute engine in 22nm SOI-CMOS achieving: (i) 1-4GS/s input, (ii) single-shot 256-length X-Corrs across all 256 lags resulting in a 256x256 X-correlator, 8.2-8.5 bit compute accuracy or hardware dynamic range (HDR) of 51-53dB, (iii) high compute efficiency of 996–1060 TOPS/W (6.6x better than SoA), (iv) high compute density of 1.3 TOPS/mm2 (7x better than SoA). We also demonstrate an X-band code-domain radar with a range resolution of 15cm across 256 range bins, supporting up to 1024 chirp averages with a 115Hz refresh rate.
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This content will become publicly available on January 10, 2026
An integrated microwave neural network for broadband computation and communication
Abstract High-bandwidth applications, from multi-gigabit communication and high-performance computing to radar signal processing, demand ever-increasing processing speeds. However, they face limitations in signal sampling and computation due to hardware and power constraints. In the microwave regime, where operating frequencies exceed the fastest clock rates, direct sampling becomes difficult, prompting interest in neuromorphic analog computing systems. We present the first demonstration of direct broadband frequency domain computing using an integrated circuit that replaces traditional analog and digital interfaces. This features a Microwave Neural Network (MNN) that operates on signals spanning tens of gigahertz, yet reprogrammed with slow, 150 MBit/sec control bitstreams. By leveraging significant nonlinearity in coupled microwave oscillators, features learned from a wide bandwidth are encoded in a comb-like spectrum spanning only a few gigahertz, enabling easy inference. We find that the MNN can search for bit sequences in arbitrary, ultra-broadband10 GBit/sec digital data, demonstrating suitability for high-speed wireline communication.Notably, it can emulate high-level digital functions without custom on-chip circuits, potentially replacing power-hungry sequential logic architectures. Its ability to track frequency changes over long capture times also allows for determining flight trajectories from radar returns. Furthermore, it serves as an accelerator for radio-frequency machine learning, capable of accurately classifying various encoding schemes used in wireless communication. The MNN achieves true, reconfigurable broadband computation, which has not yet been demonstrated by classical analog modalities, quantum reservoir computers using superconducting circuits, or photonic tensor cores, and avoidsthe inefficiencies of electro-optic transduction. Its sub-wavelength footprint in a Complementary Metal-Oxide-Semiconductor process and sub-200 milliwatt power consumption enable seamless integration as a general-purpose analog neural processor in microwave and digital signal processing chips.
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- Award ID(s):
- 2123862
- PAR ID:
- 10608246
- Publisher / Repository:
- Research Square
- Date Published:
- Format(s):
- Medium: X
- Institution:
- Research Square
- Sponsoring Org:
- National Science Foundation
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