The analog-to-digital converter (ADC) in a radio frequency (RF) front-end and digital signal processing (DSP) are significant sources of energy consumption, particularly in low-power systems like LoRa. To reliably demodulate weak signals, current systems rely on heavy oversampling - often 8x the signal bandwidth - which imposes a substantial and persistent oversampling tax on the analog front-end and DSP. This paper investigates if this tax can be mitigated by adapting techniques from image and video super resolution. We propose RF Super Resolution, a lightweight, real-time neural upscaler for RF signals. Our approach pairs an efficient digital interpolation algorithm with a shallow four-layer CNN. The neural network is trained to learn and correct the residual artifacts introduced by the digital upsampling and noise, effectively mimicking the output of a high-rate analog ADC and denoising filter. We validate our system on a large-scale, over-the-air LoRa study. Our results show that RF-SR, given a 2× Nyquist input (250 kHz), restores demodulation performance of a native 8× oversampled (2 MHz) system at half its sampling rate (1 MHz). This effectively removes the analog oversampling requirement, and provides an additional 1.25 dB SNR gain over the oversampled baseline, making it an efficient and effective signal enhancer suitable for gateway integration or post training quantized deployment at the end-node.

Dynamic Spectrum Sharing (DSS) is essential for optimizing spectrum utilization in modern wireless systems, but it requires high analog-to-digital converter (ADC) sampling rates, leading to increased costs and power consumption. Existing sub-Nyquist sampling techniques partially address these issues but struggle with dense spectrum adaptability, real-time performance, and efficiency. This paper presents RippleSense, a scalable and efficient wideband spectrum sensing approach capable of capturing GHz of densely occupied spectrum at sub-Nyquist ADC sampling rates. A novel sub-Nyquist sampling method is introduced by injecting distinct signatures into observed signals over different Nyquist zones before sampling, allowing programmatically reconstructing the full spectrum even after the Nyquist zones are folded to baseband due to inadequate ADC sampling rates. To showcase the scalability of this approach, a high-performance, multi-GHz spectrum sensing platform is implemented together with a highly parallelizable reconstruction algorithm that can process the data stream in real-time. Experimental evaluation has shown that the proposed approach supports operating configurations with signal-to-noise ratios as low as -10 dB and time resolutions down to 10 ns, enabling the capture of single radar pulses across bandwidths of up to 10 GHz using our prototype.

Spatial computing devices have been shown to significantly accelerate stencil computations, but have so far relied on unrolling the iterative dimension of a single stencil operation to increase temporal locality. This work considers the general case of mapping directed acyclic graphs of heterogeneous stencil computations to spatial computing systems, assuming large input programs without an iterative component. StencilFlow maximizes temporal locality and ensures deadlock freedom in this setting, providing end-to-end analysis and mapping from a high-level program description to distributed hardware. We evaluate our generated architectures on a Stratix 10 FPGA testbed, yielding 1.31 TOp/s and 4.18 TOp/s on single-device and multi-device, respectively, demonstrating the highest performance recorded for stencil programs on FPGAs to date. We then leverage the framework to study a complex stencil program from a production weather simulation application. Our work enables productively targeting distributed spatial computing systems with large stencil programs, and offers insight into architecture characteristics required for their efficient execution in practice.