Selected work

Representative Papers

smallNet: Implementation of a convolutional layer in tiny FPGAs

Sep 29, 2025

Deploying the first layer of CNNs on resource-constrained embedded platforms (e.g., FPGAs, SoMs, SoCs, ASICs) faces challenges including high power consumption, limited real-time performance, and dependence on Python-based or HLS toolchains. To address these, this paper proposes smallNet—a compact, hand-coded Verilog convolutional layer architecture. smallNet employs fixed-point arithmetic and a filter-like polynomial structure, requiring no Xilinx IP cores, VLSI design tools, or external Python libraries, thereby enhancing hardware portability and deployment flexibility. Evaluated on a single-core Xilinx Zynq-7000 Cora Z7 platform, smallNet achieves 81.2% classification accuracy, delivers a 5.1× speedup over CPU-based inference, and consumes only 1.5 W total system power. This work establishes a lightweight, self-contained, and synthesizable hardware implementation paradigm for low-power, real-time edge intelligence.

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On the Shape of Latent Variables in a Denoising VAE-MoG: A Posterior Sampling-Based Study

Sep 29, 2025

This study investigates the reliability of latent representations learned by a denoising variational autoencoder (VAE-MoG) on the gravitational-wave event GW150914—specifically, whether the encoder’s output faithfully captures the true posterior structure despite high signal reconstruction fidelity. Method: We introduce Hamiltonian Monte Carlo (HMC) sampling from the exact posterior given clean input data and statistically compare these samples against encoder-inferred latent distributions. Contribution/Results: Experiments reveal substantial distributional mismatch between the encoder’s latent outputs and the ground-truth posterior, demonstrating that reconstruction accuracy alone overestimates latent-space trustworthiness. This work presents the first application of HMC-based posterior sampling to validate VAE latent spaces in gravitational-wave analysis. It exposes critical limitations of conventional evaluation paradigms and advocates posterior consistency—i.e., alignment between the generative model’s implied posterior and the true posterior—as a rigorous new evaluation criterion. The framework provides methodological grounding for interpretable latent-variable modeling in gravitational-wave data analysis.

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Recent publications

Latest Papers

smallNet: Implementation of a convolutional layer in tiny FPGAs

Sep 29, 2025

Deploying the first layer of CNNs on resource-constrained embedded platforms (e.g., FPGAs, SoMs, SoCs, ASICs) faces challenges including high power consumption, limited real-time performance, and dependence on Python-based or HLS toolchains. To address these, this paper proposes smallNet—a compact, hand-coded Verilog convolutional layer architecture. smallNet employs fixed-point arithmetic and a filter-like polynomial structure, requiring no Xilinx IP cores, VLSI design tools, or external Python libraries, thereby enhancing hardware portability and deployment flexibility. Evaluated on a single-core Xilinx Zynq-7000 Cora Z7 platform, smallNet achieves 81.2% classification accuracy, delivers a 5.1× speedup over CPU-based inference, and consumes only 1.5 W total system power. This work establishes a lightweight, self-contained, and synthesizable hardware implementation paradigm for low-power, real-time edge intelligence.

0 citationsRead paper

On the Shape of Latent Variables in a Denoising VAE-MoG: A Posterior Sampling-Based Study

Sep 29, 2025

This study investigates the reliability of latent representations learned by a denoising variational autoencoder (VAE-MoG) on the gravitational-wave event GW150914—specifically, whether the encoder’s output faithfully captures the true posterior structure despite high signal reconstruction fidelity. Method: We introduce Hamiltonian Monte Carlo (HMC) sampling from the exact posterior given clean input data and statistically compare these samples against encoder-inferred latent distributions. Contribution/Results: Experiments reveal substantial distributional mismatch between the encoder’s latent outputs and the ground-truth posterior, demonstrating that reconstruction accuracy alone overestimates latent-space trustworthiness. This work presents the first application of HMC-based posterior sampling to validate VAE latent spaces in gravitational-wave analysis. It exposes critical limitations of conventional evaluation paradigms and advocates posterior consistency—i.e., alignment between the generative model’s implied posterior and the true posterior—as a rigorous new evaluation criterion. The framework provides methodological grounding for interpretable latent-variable modeling in gravitational-wave data analysis.

0 citationsRead paper