srimat t chakradhar Archives | NEC Labs America

iRAG: An Incremental Retrieval Augmented Generation System for Videos

April 24, 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 combined understanding of multimodal data such as text, images and videos is appealing but two critical limitations exist: one-time, upfront capture of all content in large multimodal data as text descriptions entails high processing times, and not all information in the rich multimodal data is typically in the text descriptions. Since the user queries are not known apriori, developing a system for multimodal to text conversion and interactive querying of multimodal data is challenging.To address these limitations, we propose iRAG, which augments RAG with a novel incremental workflow to enable interactive querying of large corpus of multimodal data. Unlike traditional RAG, iRAG quickly indexes large repositories of multimodal data, and in the incremental workflow, it uses the index to opportunistically extract more details from select portions of the multimodal data to retrieve context relevant to an interactive user query. Such an incremental workflow avoids long multimodal to text conversion times, overcomes information loss issues by doing on-demand query-specific extraction of details in multimodal data, and 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 large, real-world multimodal data. Experimental results on real-world long videos demonstrate 23x to 25x faster video to text ingestion, while ensuring that 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 querying.

LARA: Latency-Aware Resource Allocator for Stream Processing Applications

March 20, 2024/in Publications/by NEC Labs America

One of the key metrics of interest for stream processing applications is latency, which indicates the total time it takes for the application to process and generate insights from streaming input data. For mission-critical video analytics applications like surveillance and monitoring, it is of paramount importance to report an incident as soon as it occurs so that necessary actions can be taken right away. Stream processing applications are typically developed as a chain of microservices and are deployed on container orchestration platforms like Kubernetes. Allocation of system resources like cpu and memory to individual application microservices has direct impact on latency. Kubernetes does provide ways to allocate these resources e.g. through fixed resource allocation or through vertical pod autoscaler (VPA), however there is no straightforward way in Kubernetes to prioritize latency for an end-to end application pipeline. In this paper, we present LARA, which is specifically designed to improve latency of stream processing application pipelines. LARA uses a regression-based technique for resource allocation to individual microservices. We implement four real-world video analytics application pipelines i.e. license plate recognition, face recognition, human attributes detection and pose detection, and show that compared to fixed allocation, LARA is able to reduce latency by up to ? 2.8X and is consistently better than VPA. While reducing latency, LARA is also able to deliver over 2X throughput compared to fixed allocation and is almost always better than VPA.

Differentiable JPEG: The Devil is in The Details

January 3, 2024/in Publications/by NEC Labs America

JPEG remains one of the most widespread lossy image coding methods. However, the non-differentiable nature of JPEG restricts the application in deep learning pipelines. Several differentiable approximations of JPEG have recently been proposed to address this issue. This paper conducts a comprehensive review of existing diff. JPEG approaches and identifies critical details that have been missed by previous methods. To this end, we propose a novel diff. JPEG approach, overcoming previous limitations. Our approach is differentiable w.r.t. the input image, the JPEG quality, the quantization tables, and the color conversion parameters. We evaluate the forward and backward performance of our diff. JPEG approach against existing methods. Additionally, extensive ablations are performed to evaluate crucial design choices. Our proposed diff. JPEG resembles the (non-diff.) reference implementation best, significantly surpassing the recent-best diff. approach by 3.47dB (PSNR) on average. For strong compression rates, we can even improve PSNR by 9.51dB. Strong adversarial attack results are yielded by our diff. JPEG, demonstrating the effective gradient approximation. Our code is available at https://github.com/necla-ml/Diff-JPEG.

Scale Up while Scaling Out Microservices in Video Analytics Pipelines

December 18, 2023/in Publications/by NEC Labs America

Modern video analytics applications comprise multiple microservices chained together as pipelines and executed on container orchestration platforms like Kubernetes. Kubernetes automatically handles the scaling of these microservices for efficient application execution. There are two popular choices for scaling microservices in Kubernetes i.e. scaling Out using Horizontal Pod Autoscaler (HPA) and scaling Up using Vertical Pod Autoscaler (VPA). Both these have been studied independently, but there isnt much prior work studying the joint scaling of these two. This paper investigates joint scaling, i.e., scaling up while scaling out (HPA) is in action. In particular, we focus on scaling up CPU resources allocated to the application microservices. We show that allocating fixed resources does not work well for different workloads for video analytics pipelines. We also show that Kubernetes VPA in conjunction with HPA does not work well for varying application workloads. As a remedy to this problem, in this paper, we propose DataX AutoScaleUp, which performs efficiently scaling up of CPU resources allocated to microservices in video analytics pipelines while Kubernetes HPA is operational. DataX AutoScaleUp uses novel techniques to adjust the allocated computing resources to different microservices in video analytics pipelines to improve overall application performance. Through real-world video analytics applications like Face Recognition and Human Attributes, we show that DataX AutoScaleUp can achieve up to 1.45X improvement in application processing rate when compared to alternative approaches with fixed CPU allocation and dynamic CPU allocation using VPA.

Semantic Multi-Resolution Communications

December 4, 2023/in Publications/by NEC Labs America

Deep learning based joint source-channel coding (JSCC) has demonstrated significant advancements in data reconstruction compared to separate source-channel coding (SSCC). This superiority arises from the suboptimality of SSCC when dealing with finite block-length data. Moreover, SSCC falls short in reconstructing data in a multi-user and/or multi-resolution fashion, as it only tries to satisfy the worst channel and/or the highest quality data. To overcome these limitations, we propose a novel deep learning multi-resolution JSCC framework inspired by the concept of multi-task learning (MTL). This proposed framework excels at encoding data for different resolutions through hierarchical layers and effectively decodes it by leveraging both current and past layers of encoded data. Moreover, this framework holds great potential for semantic communication, where the objective extends beyond data reconstruction to preserving specific semantic attributes throughout the communication process. These semantic features could be crucial elements such as class labels, essential for classification tasks, or other key attributes that require preservation. Within this framework, each level of encoded data can be carefully designed to retain specific data semantics. As a result, the precision of a semantic classifier can be progressively enhanced across successive layers, emphasizing the preservation of targeted semantics throughout the encoding and decoding stages. We conduct experiments on MNIST and CIFAR10 dataset. The experiment with both datasets illustrates that our proposed method is capable of surpassing the SSCC method in reconstructing data with different resolutions, enabling the extraction of semantic features with heightened confidence in successive layers. This capability is particularly advantageous for prioritizing and preserving more crucial semantic features within the datasets.

Deep Learning-Based Real-Time Quality Control of Standard Video Compression for Live Streaming

November 21, 2023/in Publications/by NEC Labs America

Ensuring high-quality video content for wireless users has become increasingly vital. Nevertheless, maintaining a consistent level of video quality faces challenges due to the fluctuating encoded bitrate, primarily caused by dynamic video content, especially in live streaming scenarios. Video compression is typically employed to eliminate unnecessary redundancies within and between video frames, thereby reducing the required bandwidth for video transmission. The encoded bitrate and the quality of the compressed video depend on encoder parameters, specifically, the quantization parameter (QP). Poor choices of encoder parameters can result in reduced bandwidth efficiency and high likelihood of non-conformance. Non-conformance refers to the violation of the peak signal-to-noise ratio (PSNR) constraint for an encoded video segment. To address these issues, a real-time deep learning-based H.264 controller is proposed. This controller dynamically estimates the optimal encoder parameters based on the content of a video chunk with minimal delay. The objective is to maintain video quality in terms of PSNR above a specified threshold while minimizing the average bitrate of the compressed video. Experimental results, conducted on both QCIF dataset and a diverse range of random videos from public datasets, validate the effectiveness of this approach. Notably, it achieves improvements of up to 2.5 times in average bandwidth usage compared to the state-of-the-art adaptive bitrate video streaming, with a negligible non-conformance probability below 10?2.

LeanContext: Cost-Efficient Domain-Specific Question Answering using LLMs

September 1, 2023/in Publications/by NEC Labs America

Question-answering (QA) is a significant application of Large Language Models (LLMs), shaping chatbot capabilities across healthcare, education, and customer service. However, widespread LLM integration presents a challenge for small businesses due to the high expenses of LLM API usage. Costs rise rapidly when domain-specific data (context) is used alongside queries for accurate domain-specific LLM responses. One option is to summarize the context by using LLMs and reduce the context. However, this can also filter out useful information that is necessary to answer some domain-specific queries. In this paper, we shift from human-oriented summarizers to AI model-friendly summaries. Our approach, LeanContext, efficiently extracts k key sentences from the context that are closely aligned with the query. The choice of k is neither static nor random; we introduce a reinforcement learning technique that dynamically determines k based on the query and context. The rest of the less important sentences are reduced using a free open source text reduction method. We evaluate LeanContext against several recent query-aware and query-unaware context reduction approaches on prominent datasets (arxiv papers and BBC news articles). Despite cost reductions of 37.29% to 67.81%, LeanContext’s ROUGE-1 score decreases only by 1.41% to 2.65% compared to a baseline that retains the entire context (no summarization). Additionally, if free pretrained LLM-based summarizers are used to reduce context (into human consumable summaries), LeanContext can further modify the reduced context to enhance the accuracy (ROUGE-1 score) by 13.22% to 24.61%.

Deep Video Codec Control

August 29, 2023/in Publications/by NEC Labs America

Deep Video Codec Control Lossy video compression is commonly used when transmitting and storing video data. Unified video codecs (e.g., H.264 or H.265) remain the emph(Unknown sysvar: (de facto)) standard, despite the availability of advanced (neural) compression approaches. Transmitting videos in the face of dynamic network bandwidth conditions requires video codecs to adapt to vastly different compression strengths. Rate control modules augment the codec’s compression such that bandwidth constraints are satisfied and video distortion is minimized. While, both standard video codes and their rate control modules are developed to minimize video distortion w.r.t. human quality assessment, preserving the downstream performance of deep vision models is not considered. In this paper, we present the first end-to-end learnable deep video codec control considering both bandwidth constraints and downstream vision performance, while not breaking existing standardization. We demonstrate for two common vision tasks (semantic segmentation and optical flow estimation) and on two different datasets that our deep codec control better preserves downstream performance than using 2-pass average bit rate control while meeting dynamic bandwidth constraints and adhering to standardizations.

Retrospective : A Dynamically Configurable Coprocessor For Convolutional Neural Networks

July 1, 2023/in Publications/by NEC Labs America

In 2008, parallel computing posed significant challenges due to the complexities of parallel programming and the bottlenecks associated with efficient parallel execution. Inspired by the remarkable scalability achieved by networking and storage systems in handling extensive packet traffic and persistent data respectively by leveraging best-effort service, we proposed a new and fundamentally different approach of best-effort computing.Having observed that a broad spectrum of existing and emerging computing workloads were from applications that had an inherent forgiving nature [2], [5], we proposed best effort computing. The new approach resulted in disproportionate gains in power, energy and latency, while improving performance. While contemplating the concept of best-effort computing [2], we noticed the resurgence of convolutional neural networks, which generated approximate but acceptable outcomes for numerous recognition, mining, and synthesis tasks. The lead author of this retrospective had previously conducted research on neural networks for his doctoral dissertation over a decade ago, and the reemergence of neural networks proved both surprising and exciting. Recognizing the connection between best-effort computing and convolutional neural networks, in 2008 we embarked on developing a programmable and dynamically reconfigurable convolutional neural network capable of performing best effort computing for various machine learning tasks that inherently allow for multiple acceptable answers. This combination of our thoughts on best-effort computing and the gradual evolution of convolutional neural networks (deep neural networks emerged much later) culminated in our 2010 ISCA work on dynamically reconfigurable convolutional neural networks.

Elixir: A System To Enhance Data Quality For Multiple Analytics On A Video Stream

June 26, 2023/in Publications/by NEC Labs America

IoT sensors, especially video cameras, are ubiquitously deployed around the world to perform a variety of computer vision tasks in several verticals including retail, health- care, safety and security, transportation, manufacturing, etc. To amortize their high deployment effort and cost, it is desirable to perform multiple video analytics tasks, which we refer to as Analytical Units (AUs), off the video feed coming out of every camera. As AUs typically use deep learning-based AI/ML models, their performance depend on the quality of the input video, and recent work has shown that dynamically adjusting the camera setting exposed by popular network cameras can help improve the quality of the video feed and hence the AU accuracy, in a single AU setting. In this paper, we first show that in a multi-AU setting, changing the camera setting has disproportionate impact on different AUs performance. In particular, the optimal setting for one AU may severely degrade the performance for another AU, and further the impact on different AUs varies as the environmental condition changes. We then present Elixir, a system to enhance the video stream quality for multiple analytics on a video stream. Elixir leverages Multi-Objective Reinforcement Learning (MORL), where the RL agent caters to the objectives from different AUs and adjusts the camera setting to simultaneously enhance the performance of all AUs. To define the multiple objectives in MORL, we develop new AU-specific quality estimator values for each individual AU. We evaluate Elixir through real-world experiments on a testbed with three cameras deployed next to each other (overlooking a large enterprise parking lot) running Elixir and two baseline approaches, respectively. Elixir correctly detects 7.1% (22,068) and 5.0% (15,731) more cars, 94% (551) and 72% (478) more faces, and 670.4% (4975) and 158.6% (3507) more persons than the default-setting and time-sharing approaches, respectively. It also detects 115 license plates, far more than the time-sharing approach (7) and the default setting (0).