High Performance Compute Accelerators for Radio Frequency Signal Processing Applications
Author(s)
Mizanur Rahman, Nael
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Abstract
Signal processing algorithms for Radio Frequency (RF) sensors, RF communication and RF interaction have seen remarkable progress, aligning with the rapid advancements in wireless communication standards. The resulting surge in data volume presents a challenge: processing this data at high throughput without causing a bottleneck. Additionally as computational models themselves become increasingly complex, there's a growing need to develop hardware capable of handling such high-throughput demands. Furthermore, in interconnected systems where data is transferred between sensor front-ends and processor back-ends, the security of this data transfer is critical, necessitating high-throughput, secure encryption solutions.
This thesis focuses on the design of scalable, high-performance compute accelerators for RF signal processing applications, with a specific focus on high-bandwidth RF emulation and Multiple Input digital RF beamforming. This is achieved by combining In-memory/Near-memory architectures with high throughput digital compute engine design.
We begin by demonstrating a Near-Memory digital compute accelerator for real-time emulation of RF interactions. This architecture allows for the simulation of complex RF interactions within dynamic environments, incorporating various physical phenomena, simultaneously providing low compute latency as well as high emulation bandwidth.
Next, we present a Compute-In-Memory based digital beamforming accelerator. This design confronts the core challenges of scalability and energy efficiency in large-scale MIMO RX digital beamformers. It achieves substantial power savings compared to traditional beamforming architectures while minimally impacting beam accuracy.
Finally we proposing security-aware pipelining techniques that use algorithmic key diffusion to enhance the throughput of PRINCE encryption accelerators while maintaining robustness against side-channel attacks. This approach is crucial for securing high-bandwidth data transmission between RF front end sensors and back-end (off-site) control units/host processors. Additionally, we introduce a custom lightweight power supply sensor that enables the detection of power side-channel attacks on encryption engines with minimal hardware overheads, further bolstering the security of these systems.
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Date
2023-12-14
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Dissertation