Abstract

A method for describing, distributing and re-producing specifications of analog IC using AI models, is fulfilled in the ongoing description by (a) creating an AI model to represent the electrical specifications of the analog integrated circuit over a specified range of PVT, input and output loading, and aging conditions,(b) training the AI model with simulation data associated with the analog IC, wherein the simulation inputs include a plurality of instances of PVT (Process, Voltage, Temperature), input/output loading, and aging conditions,(c) electronically enabling peripheral functions with the AI model into an AI model pack, wherein the peripheral functions include a set of computer code modules to extract the AI model, perform user requested operations, calculate specifications of the analog IC on-the-fly and interact with the user, and (d) distributing the encrypted AI model pack to a plurality of users.

Abstract

Drivable Freespace prediction is a fundamental and crucial problem in autonomous driving. Recent works have addressed the problem by representing the entire non-obstacle road regions as the freespace. In contrast our aim is to estimate the driving corridors that are a navigable subset of the entire road region. Unfortunately, existing corridor estimation methods directly assume a BEV centric representation, which is hard to obtain. In contrast, we frame drivable freespace corridor prediction as a pure image perception task, using only monocular camera input. However such a formulation poses several challenges as one doesn’t have the corresponding data for such freespace corridor segments in the image. Consequently, we develop a novel self-supervised approach for freespace sample generation by leveraging future ego trajectories and front-view camera images, making the process of visual corridor estimation dependent on the ego trajectory. We then employ a diffusion process to model the distribution of such segments in the image. However, the existing binary mask based representation for a segment poses many limitations. Therefore, we introduce ContourDiff, a specialized diffusion-based architecture that denoises over contour points rather than relying on binary mask representations, enabling structured and interpretable freespace predictions. We evaluate our approach qualitatively and quantitatively on both nuScenes and CARLA, demonstrating its effectiveness in accurately predicting safe multimodal navigable corridors in the image.

Abstract

SparseLoc is a global localization framework for city-scale scenarios that leverages vision-language foundation models to generate sparse semantic topometric maps in a zero-shot manner. It combines this map representation with a Monte Carlo localization scheme enhanced by a novel late optimization strategy, ensuring improved pose estimation. By constructing compact yet highly discriminative maps and refining localization through a carefully designed optimization schedule, SparseLoc overcomes the limitations of existing topometric localization methods, offering a better solution for global localization at the scale of a city. Our system achieves over a 5× improvement in localization accuracy compared to existing sparse mapping techniques. Despite utilizing only 1/500th of the points of dense mapping methods, it achieves comparable performance, maintaining an average global localization error below 5m and 2° on KITTI Sequences.

Abstract

This paper explores advanced voltage reference designs with extremely low power consumption, specifically under one microwatt. These designs are crucial for portable electronic systems that rely on minimal power. The paper categorizes these voltage references based on the types of devices used to generate temperature-proportional or complementary signals. It also provides key design examples to illustrate these concepts. Additionally, the paper enhances understanding by conducting a comparative analysis of performance metrics, including power consumption, temperature coefficient, physical size, and resistance to variations in the manufacturing process.

Abstract

This paper presents a dual-loop reference-less Clock and Data Recovery (CDR) architecture implemented in TSMC 28nm CMOS process for PCIe Gen4 application. A novel frequency detector (FD) with a wide frequency acquisition range is presented, which avoids harmonic locking issues and is independent of input transition density. The FD demonstrates rapid locking capabilities, securing synchronization with Pseudo-Random Binary Sequences (PRBS7 and PRBS31) data patterns. The locking time from PCIe Gen1 (2.5Gbps) to PCIe Gen4 (16Gbps) is less than 5us. The CDR achieves a power consumption of less than 2mW with a supply voltage of 0.9V.

Abstract

We present a transformer architecture-based foundation model for tasks at high-energy particle colliders such as the Large Hadron Collider. We train the model to classify jets using a self-supervised strategy inspired by the Joint Embedding Predictive Architecture. We use the JetClass dataset containing 100M jets of various known particles to pre-train the model with a data-centric approach -- the model uses a fraction of the jet constituents as the context to predict the embeddings of the unseen target constituents. Our pre-trained model fares well with other datasets for standard classification benchmark tasks. We test our model on two additional downstream tasks: top tagging and differentiating light-quark jets from gluon jets. We also evaluate our model with task-specific metrics and baselines and compare it with state-of-the-art models in high-energy physics. Project site: this https URL

Abstract

This paper addresses the challenge of water dis- aggregation in intermediate water supply (IWS) systems using water level data. The water level node, in conjunction with four automatic labelling nodes, was deployed to collect data on appliance usage, no activity periods, and tank filling. Data is collected and transmitted to the cloud at two-second intervals via Wi-Fi networks. The collected water level data are used to train various machine learning (ML) and deep learning (DL) models for disaggregation in filling, appliance use such as geyser, flush, washing machine, and periods of no activity. These models are then evaluated to determine the most effective approach for water disaggregation, with performance assessed using accuracy and F1 score metrics. Out of the tested models, the Long Short- Term Memory (LSTM) model emerged as the best-performing algorithm, achieving an accuracy of 94.09% and an F1 score of 0.88.

Abstract

Large Language Models (LLMs) have demonstrated strong capabilities as autonomous agents through tool use, planning, and decision-making abilities, leading to their widespread adoption across diverse tasks. As task complexity grows, multi-agent LLM systems are increasingly used to collaboratively solve problems. However, safety and security of these multi-agent systems remains largely unexplored. Existing benchmarks and datasets predominantly focus on single-agent settings, failing to capture the unique vulnerabilities of multi-agent dynamics and co-ordination. To address this gap, we introduce textbf{T}hreats and textbf{A}ttacks in textbf{M}ulti-textbf{A}gent textbf{S}ystems (textbf{TAMAS}), a dataset designed to evaluate the robustness and security of multi-agent LLM systems. TAMAS includes five distinct scenarios comprising 250 adversarial instances across five attack types and 163 different normal and attack tools, along with 100 harmless tasks. We assess system performance across 5 backbone LLMs and 3 agent interaction configurations from Autogen framework, highlighting critical challenges and failure modes in current multi-agent deployments. Our findings show that multi-agent systems are highly vulnerable to adversarial attacks, with Impersonation reaching a 73% success rate and other attacks ranging from 27% to 67%, underscoring the need for stronger defenses.

Abstract

Algorithms are a fundamental part of computer science education, with expressing and tracing forming key aspects of their study. Both pseudocode and code are used to express algorithms. Pseudocode helps abstract away specifics of the programming language when specifying an algorithm, but it can introduce ambiguities due to its informal nature and does not support a structured way to trace an algorithm. On the other hand, tracing a program provides a precise description but can conflate algorithmic logic with language-specific details, obscuring the inherent structure of an algorithm. In this experience report, we propose employing the Mapcode framework for expressing and tracing algorithms. Mapcode represents an algorithm as a set of transformations between well-defined spaces, naturally producing execution traces that are independent of programming constructs. Unlike traditional notional machines, which model the execution of programs, Mapcode machines model the high-level execution of algorithms. Mapcode traces reflect underlying logic and are independent of programming-language-specific data and control structures like while loops. We report preliminary findings from two classroom activities which explored the application of Mapcode to express and trace algorithms. In the first activity, third-year undergraduate students drew Mapcode Execution Diagrams to trace simple algorithms already specified in the Mapcode framework. We found that a majority of students were successful in constructing the diagrams. In the second activity, students in a Principles of Programming Languages course successfully defined algorithms from various paradigms (greedy, dynamic programming, sorting, etc.) as Mapcode machines and implemented them in Python and Racket.

Abstract

Effective management and optimization of urban infrastructures necessitates scalable and accessible simulation frameworks. Modern BIM-based solutions present a promising avenue for novel construction projects; however, these solutions are often inapplicable to the extensive array of legacy infrastructures developed prior to the establishment of BIM as a construction standard. Commensurate with this, we present a new modeldriven simulation approach for urban infrastructures that (i) utilizes a novel domain-specific modeling language (DSML) to represent both structural and behavioral characteristics of these infrastructures and (ii) employs the Discrete Event System Specification (DEVS) formalism for simulation purposes. Reframing urban infrastructures as IoT-based, event-driven systems facilitates efficient, hierarchical simulations of complex dynamic environments, including resource management and water networks. Simulation artifacts produced from the DSML through model-to-text generation are executed within the DEVS simulation framework. We validate our approach through a case study conducted at IIIT Hyderabad's Smart City Living Lab, illustrating its capacity to identify optimization opportunities within urban infrastructures.