arXiv subjects

Aakash Patel

Publications and source records attributed to Aakash Patel.

3 recordsLinked to original sources

arXiv2026

Technology solutions targeting the performance of gen-AI inference in resource constrained platforms

The rise of generative AI workloads, particularly language model inference, is intensifying on/off-chip memory pressure. Multimodal inputs such as video streams or images and downstream applications like Question Answering (QA) and analysis over large documents incur long context lengths, requiring caching of massive Key and Value states of the previous tokens. Even a low degree of concurrent inference serving on resource-constrained devices, like mobiles, can further add to memory capacity pressure and runtime memory management complexity. In this paper, we evaluate the performance implications of two emerging technology solutions to alleviate the memory pressure in terms of both capacity and bandwidth using a hierarchical roofline-based analytical performance model. For large models (e.g., 13B parameters) and context lengths, we investigate the performance implications of High Bandwidth Storage (HBS) and outline bandwidth/latency requirements to achieve an acceptable throughput for interactivity. For small models (e.g., 1B parameters), we evaluate the merit of a bonded global buffer memory chiplet and propose how to best utilize it.

cs.AR↗

arXiv2025

Neural Posterior Estimation for Cataloging Astronomical Images with Spatially Varying Backgrounds and Point Spread Functions

Neural posterior estimation (NPE), a type of amortized variational inference, is a computationally efficient means of constructing probabilistic catalogs of light sources from astronomical images. To date, NPE has not been used to perform inference in models with spatially varying covariates. However, ground-based astronomical images have spatially varying sky backgrounds and point spread functions (PSFs), and accounting for this variation is essential for constructing accurate catalogs of imaged light sources. In this work, we introduce a method of performing NPE with spatially varying backgrounds and PSFs. In this method, we generate synthetic catalogs and semi-synthetic images for these catalogs using randomly sampled PSF and background estimates from existing surveys. Using this data, we train a neural network, which takes an astronomical image and representations of its background and PSF as input, to output a probabilistic catalog. Our experiments with Sloan Digital Sky Survey data demonstrate the effectiveness of NPE in the presence of spatially varying backgrounds and PSFs for light source detection, star/galaxy separation, and flux measurement.

astro-ph.IM↗

arXiv2024

Intelligence at the Edge of Chaos

We explore the emergence of intelligent behavior in artificial systems by investigating how the complexity of rule-based systems influences the capabilities of models trained to predict these rules. Our study focuses on elementary cellular automata (ECA), simple yet powerful one-dimensional systems that generate behaviors ranging from trivial to highly complex. By training distinct Large Language Models (LLMs) on different ECAs, we evaluated the relationship between the complexity of the rules' behavior and the intelligence exhibited by the LLMs, as reflected in their performance on downstream tasks. Our findings reveal that rules with higher complexity lead to models exhibiting greater intelligence, as demonstrated by their performance on reasoning and chess move prediction tasks. Both uniform and periodic systems, and often also highly chaotic systems, resulted in poorer downstream performance, highlighting a sweet spot of complexity conducive to intelligence. We conjecture that intelligence arises from the ability to predict complexity and that creating intelligence may require only exposure to complexity.

cs.AI↗