srimat t chakradhar Archives | NEC Labs America

LLM-based Distributed Code Generation and Cost-Efficient Execution in the Cloud

April 6, 2025/in Publications/by NEC Labs America

The advancement of Generative Artificial Intelligence (AI), particularly Large Language Models (LLMs), is reshaping the software industry by automating code generation. Many LLM-driven distributed processing systems rely on serial code generation constrained by predefined libraries, limiting flexibility and adaptability. While some approaches enhance performance through parallel execution or optimize edge-cloud distributed processing for specific domains, they often overlook the cost implications of deployment, restricting scalability and economic feasibility across diverse cloud environments. This paper presents DiCE-C, a system that eliminates these constraints by starting directly from a natural language query. DiCE-C dynamically identifies available tools at runtime, programmatically refines LLM prompts, and employs a stepwise approachfirst generating serial code and then transforming it into distributed code. This adaptive methodology enables efficient distributed execution without dependence on specific libraries. By leveraging high-level parallelism at the Application Programming Interface (API) level and managing API execution as services within a Kubernetes-based runtime, DiCE-C reduces idle GPU time and facilitates the use of smaller, cost-effective GPU instances. Experiments with a vision-based insurance application demonstrate that DiCE-C reduces cloud operational costs by up to 72% when using smaller GPUs (A6000 and A4000 GPU machines vs. A100 GPU machine) and by 32% when using identical GPUs (A100 GPU machines). This flexible and cost-efficient approach makes DiCE-C a scalable solution for deploying LLM-generated vision applications in cloud environments.

Real-Time Network-Aware Roadside LiDAR Data Compression

April 2, 2025/in Publications/by NEC Labs America

LiDAR technology has emerged as a pivotal tool in Intelligent Transportation Systems (ITS), providing unique capabilities that have significantly transformed roadside traffic applications. However, this transformation comes with a distinct challenge: the immense volume of data generated by LiDAR sensors. These sensors produce vast amounts of data every second, which can overwhelm both private and public 5G networks that are used to connect intersections. This data volume makes it challenging to stream raw sensor data across multiple intersections effectively. This paper proposes an efficient real-time compression method for roadside LiDAR data. Our approach exploits a special characteristic of roadside LiDAR data: the background points are consistent across all frames. We detect these background points and send them to edge servers only once. For each subsequent frame, we filter out the background points and compress only the remaining data. This process achieves significant temporal compression by eliminating redundant background data and substantial spatial compression by focusing only on the filtered points. Our method is sensor-agnostic, exceptionally fast, memory-efficient, and adaptable to varying network conditions. It offers a 2.5x increase in compression rates and improves application-level accuracy by 40% compared to current state-of-the-art methods.

CAMTUNER: Adaptive Video Analytics Pipelines via Real-time Automated Camera Parameter Tuning

March 31, 2025/in Publications/by NEC Labs America

In Video Analytics Pipelines (VAP), Analytics Units (AUs) such as object detection and face recognition operating on remote servers rely heavily on surveillance cameras to capture high-quality video streams to achieve high accuracy. Modern network cameras offer an array of parameters that directly influence video quality. While a few of such parameters, e.g., exposure, focus and white balance, are automatically adjusted by the camera internally, the others are not. We denote such camera parameters as non-automated (NAUTO) parameters. In this work, we first show that in a typical surveillance camera deployment, environmental condition changes can have significant adverse effect on the accuracy of insights from the AUs, but such adverse impact can potentially be mitigated by dynamically adjusting NAUTO camera parameters in response to changes in environmental conditions. Second, since most end-users lack the skill or understanding to appropriately configure these parameters and typically use a fixed parameter setting, we present CAMTUNER, to our knowledge, the first framework that dynamically adapts NAUTO camera parameters to optimize the accuracy of AUs in a VAP in response to adverse changes in environmental conditions. CAMTUNER is based on SARSA reinforcement learning and it incorporates two novel components: a light-weight analytics quality estimator and a virtual camera that drastically speed up offline RL training. Our controlled experiments and real-world VAP deployment show that compared to a VAP using the default camera setting, CAMTUNER enhances VAP accuracy by detecting 15.9% additional persons and 2.6%-4.2% additional cars (without any false positives) in a large enterprise parking lot. CAMTUNER opens up new avenues for elevating video analytics accuracy, transcending mere incremental enhancements achieved through refining deep-learning models.

EdgeSync: Efficient Edge-Assisted Video Analytics via Network Contention-Aware Scheduling

March 17, 2025/in Publications/by NEC Labs America

With the advancement of 5G, edge-assisted video analytics has become increasingly popular, driven by the technologys ability to support low-latency, high-bandwidth applications. However, in scenarios where multiple clients competing for network resources, network contention poses a significant challenge. In this paper, we propose a novel scheduling algorithm that intelligently batches and aligns the offloading of multiple video analytics clients to optimize both network and edge server resource utilization while meeting the Service Level Objective (SLO). Experiment with a cellular network testbed shows that our approach successfully processes 93% or more of inference requests from 7 different clients to the edge server while meeting the SLOs, whereas other approaches achieve a lower success rate, ranging from 65% to 85% under the same condition.

RAG-check: Evaluating Multimodal Retrieval Augmented Generation Performance

January 7, 2025/in Publications/by NEC Labs America

Retrieval-augmented generation (RAG) improves large language models (LLMs) by using external knowledge to guide response generation, reducing hallucinations. However, RAG, particularly multi-modal RAG, can introduce new hallucination sources: (i) the retrieval process may select irrelevant pieces (e.g., documents, images) as raw context from the database, and (ii) retrieved images are processed into text-based context via vision-language models (VLMs) or directly used by multi-modal language models (MLLMs) like GPT-4o, which may hallucinate. To address this, we propose a novel framework to evaluate the reliability of multi-modal RAG using two performance measures: (i) the relevancy score (RS), assessing the relevance of retrieved entries to the query, and (ii) the correctness score (CS), evaluating the accuracy of the generated response. We train RS and CS models using a ChatGPT-derived database and human evaluator samples. Results show that both models achieve ~88% accuracy on test data. Additionally, we construct a 5000-sample human-annotated database evaluating the relevancy of retrieved pieces and the correctness of response statements. Our RS model aligns with human preferences 20% more often than CLIP in retrieval, and our CS model matches human preferences ~91% of the time. Finally, we assess various RAG systems’ selection and generation performances using RS and CS.

Re-ranking the Context for Multimodal Retrieval Augmented Generation

January 6, 2025/in Publications/by NEC Labs America

Retrieval-augmented generation (RAG) enhances large language models (LLMs) by incorporating external knowledge to generate a response within a context with improved accuracy and reduced hallucinations. However, multi-modal RAG systems face unique challenges: (i) the retrieval process may select irrelevant entries to user query (e.g., images, documents), and (ii) vision-language models or multi-modal language models like GPT-4o may hallucinate when processing these entries to generate RAG output. In this paper, we aim to address the first challenge, i.e, improving the selection of relevant context from the knowledge-base in retrieval phase of the multi-modal RAG. Specifically, we leverage the relevancy score (RS) measure designed in our previous work for evaluating the RAG performance to select more relevant entries in retrieval process. The retrieval based on embeddings, say CLIP-based embedding, and cosine similarity usually perform poorly particularly for multi-modal data. We show that by using a more advanced relevancy measure, one can enhance the retrieval process by selecting more relevant pieces from the knowledge-base and eliminate the irrelevant pieces from the context by adaptively selecting up-to-k entries instead of fixed number of entries. Our evaluation using COCO dataset demonstrates significant enhancement in selecting relevant context and accuracy of the generated response.

DiCE-M: Distributed Code Generation and Execution for Marine Applications – An Edge-Cloud Approach

December 7, 2024/in Publications/by NEC Labs America

Edge computing has emerged as a transformative technology that reduces application latency, improves cost efficiency, enhances security, and enables large-scale deployment of applications across various domains. In environmental monitoring, systems such as MegaSense[49], use low-cost sensors to gather and process real-time air quality data through edge-cloud collaboration, highlighting the critical role of edge computing in enabling scalable, efficient solutions. Similarly, marine science increasingly requires real-time processing and analysis of marine data from remote, resource-constrained environments. In this paper, we extend the power of edge computing by integrating it with Generative Artificial Intelligence(GenAI),specifically large language models (LLMs), to address challenges in marine science applications. We propose DiCE-M (Distributed Code generation and Execution for Marine applications), a robust system that uses LLM to generate distributed code for marine applications and then utilizes a runtime to efficiently execute it on an edge+cloud computing infrastructure. Specifically, DiCE-M leverages edge computing to execute lightweight AI models locally on unmanned surface vehicles(USVs)while offloading complex tasks to the cloud, thus balancing computational load and enabling realtime monitoring in marine environments. We use marine litter identification as an example application to demonstrate the utility of DiCE-M. Our results show that DiCE-M reduces latency by more than 2X when marine litter is not detected and cuts cloud computing costs by more than half compared to traditional cloud-based approaches. By selectively cropping and transmitting relevant image portions, DiCE-M further improves bandwidth efficiency, making it a reliable and cost-effective solution for deploying AI-driven applications on resource-constrained USVs in dynamic marine environments.

DiCE: Distributed Code generation and Execution

November 5, 2024/in Publications/by NEC Labs America

Generative artificial intelligence (GenAI), specifically, Large Language Models (LLMs), have shown tremendous potential in automating several tasks and improving human productivity. Recent works have shown them to be quite useful in writing and summarizing text (articles, blogs, poems, stories, songs, etc.), answering questions, brainstorming ideas, and even writing code. Several LLMs have emerged specifically targeting code generation. Given a prompt, these LLMs can generate code in any desired programming language. Many tools like ChatGPT, CoPilot, CodeWhisperer, Cody, DeepSeek Coder, StarCoder, etc. are now routinely being used by software developers. However, most of the prior work in automatic code generation using LLMs is focused on obtaining correct and working code, and mainly runs on a single computer (serial code). In this paper, we take this to the next level, where LLMs are leveraged to generate code for execution on a distributed infrastructure. We propose a novel system called DiCE, which takes serial code as input and automatically generates distributed version of the code and efficiently executes it on a distributed setup. DiCE consists of two main components (a) LLM-based tool (Synthia) to understand dependencies in serial code and automatically generate distributed version of the code using specialized programming model and semantics, and (b) Runtime (Hermod) to understand the semantics in the distributed code and realize efficient execution on a cluster of machines (distributed infrastructure). DiCE currently focuses on visual programs synthesized by tools like ViperGPT [1] and VisReP [2] (serial code), automatically identifies higher-level task parallelism opportunities (e.g., parallel object detection), transforms the code to exploit the parallelism, and finally efficiently executes it on a cluster of machines. Through our experiments using 100 examples from the GQA dataset [3], we show that the serial codes generated by ViperGPT are successfully transformed into distributed codes which are then efficiently executed on a cluster of machines by DiCE. We note that DiCE correctly identifies opportunities for parallelism and distributes tasks on separate GPUs within the cluster. We observe an average speed-up of 2X, 2.95X, and 3.7X, and an average efficiency of 1, 0.74 and 0.48 for a cluster of 2 nodes, 4 nodes, and 8 nodes, respectively.

iRAG: Advancing RAG for Videos with an Incremental Approach

October 21, 2024/in Publications/by NEC Labs America

Retrieval-augmented generation (RAG) systems combine the strengths of language generation and information retrieval to power many real-world applications like chatbots. Use of RAG for understanding of videos is appealing but there are two critical limitations. One-time, upfront conversion of all content in large corpus of videos into text descriptions entails high processing times. Also, not all information in the rich video data is typically captured in the text descriptions. Since user queries are not known apriori, developing a system for video to text conversion and interactive querying of video data is challenging.To address these limitations, we propose an incremental RAG system called iRAG, which augments RAG with a novel incremental workflow to enable interactive querying of a large corpus of videos. Unlike traditional RAG, iRAG quickly indexes large repositories of videos, and in the incremental workflow, it uses the index to opportunistically extract more details from select portions of the videos to retrieve context relevant to an interactive user query. Such an incremental workflow avoids long video to text conversion times, and overcomes information loss issues due to conversion of video to text, by doing on-demand query-specific extraction of details in video data. This ensures high quality of responses to interactive user queries that are often not known apriori. To the best of our knowledge, iRAG is the first system to augment RAG with an incremental workflow to support efficient interactive querying of a large corpus of videos. Experimental results on real-world datasets demonstrate 23x to 25x faster video to text ingestion, while ensuring that latency and quality of responses to interactive user queries is comparable to responses from a traditional RAG where all video data is converted to text upfront before any user querying.

TrafficLens: Multi-Camera Traffic Video Analysis Using LLMs

September 24, 2024/in Publications/by NEC Labs America

Traffic cameras are essential in urban areas, playing a crucial role in intelligent transportation systems. Multiple cameras at intersections enhance law enforcement capabilities, traffic management, and pedestrian safety. However, efficiently managing and analyzing multi-camera feeds poses challenges due to the vast amount of data. Analyzing such huge video data requires advanced analytical tools. While Large Language Models (LLMs) like ChatGPT, equipped with retrieval-augmented generation (RAG) systems, excel in text-based tasks, integrating them into traffic video analysis demands converting video data into text using a Vision-Language Model (VLM), which is time-consuming and delays the timely utilization of traffic videos for generating insights and investigating incidents. To address these challenges, we propose TrafficLens, a tailored algorithm for multi-camera traffic intersections. TrafficLens employs a sequential approach, utilizing overlapping coverage areas of cameras. It iteratively applies VLMs with varying token limits, using previous outputs as prompts for subsequent cameras, enabling rapid generation of detailed textual descriptions while reducing processing time. Additionally, TrafficLens intelligently bypasses redundant VLM invocations through an object-level similarity detector. Experimental results with real-world datasets demonstrate that TrafficLens reduces video-to-text conversion time by up to 4× while maintaining information accuracy.