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Vision Transformer-Based Automated Classification of Acute Lymphoblastic Leukemia 5

Vision Transformer-Based Automated Classification of Acute L...

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5th international Conference on Emerging Systems and Intelligent computing, ESIC 2025

作者： Swain, K.P. Swain, S.K. Nayak, S.R. Deemed to be University School of Computer Engineering Bhubaneswar751024 India

ISBN: (纸本)9798331522100

ALL, or Acute Lymphoblastic Leukaemia, is an acute leukaemia disease characterized by white blood cell malignancy, most commonly found in children. Conventional diagnosis requires higher-order laboratory testing procedures that are rather cumbersome and costly. In recent times, deep learning research has marked outstanding performances for automation in medical image analyses that facilitate quick and correct diagnosis. In this work, we proposed a deep learning-based model with the Vision Transformer architecture to classify ALL from peripheral blood smear images. It performed very well on the test set with an accuracy of 99.69% and hence proved to be efficient in identifying ALL with a high degree of precision. Additionally, we employed Gradient-weighted Class Activation Mapping (Grad-CAM) to enhance the explainability of the model's predictions, providing visual insights into the regions of blood smear images that influenced the classification decisions. This integration of explainable AI (XAI) ensures transparency and builds trust in the model's predictions, making it more clinically acceptable. The performance and interpretability of the ViT-based model indicate that it may revolutionize the diagnostic practices in hematological malignancies by offering a scalable, efficient, and explainable diagnostic solution. © 2025 ieee.

关键词： Diagnostic radiography

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CORDOBA: Carbon-Efficient Optimization Framework for computing Systems

CORDOBA: Carbon-Efficient Optimization Framework for Computi...

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ieee symposium on high-performance computer architecture

作者： Mariam Elgamal Doug Carmean Elnaz Ansari Okay Zed Ramesh Peri Srilatha Manne Udit Gupta Gu-Yeon Wei David Brooks Gage Hills Carole-Jean Wu Harvard University Meta

ISBN: (数字)9798331506476

ISBN: (纸本)9798331506483

The world’s push toward an environmentally sustainable society is highly dependent on the semiconductor industry. Despite existing carbon modeling efforts to quantify carbon footprint of computing systems, optimizing carbon footprint in large design spaces-while also considering trade-offs in power, performance, and area-is especially challenging. To address this need, we present CORDOBA, a carbon-aware optimization framework that optimizes carbon efficiency. We quantify carbon efficiency using the total Carbon Delay Product metric (tCDP): the product of total carbon and application execution time. We justify why tCDP is an effective metric for quantifying carbon efficiency. We use CORDOBA to explore the large design space for carbonefficient specialized hardware, and identify distinct carbonefficient optimal designs across operational use (eliminating up to $\mathbf{9 8 \%}$ of the design space) despite uncertainty in carbon footprint parameters. We quantify opportunities to improve tCDP for real system case studies: (a) optimizing hardware provisioning from 8 to 4 cores in real system CPUs improves tCDP by $1.25 \times$; and (b) leveraging advanced three-dimensional (3D) integration techniques (3D stacking of separately-fabricated logic and memory chips) improves tCDP by $6.9 \times$ versus conventional systems.

关键词： Measurement Uncertainty Three-dimensional displays Computational modeling Hardware Carbon Optimization Carbon footprint Three-dimensional integrated circuits System analysis and design

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Are GAFs All you Need for BCIs?

Are GAFs All you Need for BCIs?

引用

ieee international symposium on Biomedical Imaging

作者： Hernandez Gomez A. Nieto N. Podesta M. O. Felix F. Munoz L. A. School of Engineering and Sciences Tec de Monterrey Monterrey N.L. México

ISBN: (数字)9798331520526

ISBN: (纸本)9798331520533

This study presents a proposal of Learnable Gramian Angular Fields (LGAFs), for Brain-computer Interface (BCI) performance in visual-motor intention classification. We introduce LGAFs with a convolutional neural network (CNN), achieving 93.06% accuracy across six hand movements. The architecture transforms time-series EEG data into optimized two-dimensional representations while utilizing CNNs' efficiency for real-time applications. Through experimentation on four subjects, we validate the model's capability to maintain consistent classification accuracy across movement types. The results demonstrate the LGAF-CNN combination's high performance and potential computational efficiency, establishing its potential for practical BCI applications. Through ablation studies and performance analysis, the architecture achieved comparable accuracy with only three optimally selected channels.

关键词： Electric potential Accuracy computer architecture Transforms Electroencephalography Brain-computer interfaces Real-time systems performance analysis Convolutional neural networks Proposals

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InstAttention: In-Storage Attention Offloading for Cost-Effective Long-Context LLM Inference

InstAttention: In-Storage Attention Offloading for Cost-Effe...

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ieee symposium on high-performance computer architecture

作者： Xiurui Pan Endian Li Qiao Li Shengwen Liang Yizhou Shan Ke Zhou Yingwei Luo Xiaolin Wang Jie Zhang Peking University University of Electronic Science and Technology of China Institute of Computing Technology Chinese Academy of Sciences Huawei Cloud Wuhan National Laboratory for Optoelectronics of Huazhong University of Science and Technology

ISBN: (数字)9798331506476

ISBN: (纸本)9798331506483

The widespread of Large Language Models (LLMs) marks a significant milestone in generative AI. Nevertheless, the increasing context length and batch size in offline LLM inference escalate the memory requirement of the key-value (KV) cache, which imposes a huge burden on the GPU VRAM, especially for resource-constrained scenarios (e.g., edge computing). Several cost-effective solutions leverage host memory or SSDs to reduce storage costs for offline inference scenarios and improve the throughput. Nevertheless, they suffer from significant performance penalties imposed by intensive KV cache accesses due to limited PCIe bandwidth. To address these issues, we propose InstAttention, a novel LLM inference system that offloads the most performance-critical computation (i.e., attention in decoding phase) and data (i.e., KV cache) parts to Computational Storage Drives (CSDs), which minimize the enormous KV transfer overheads. InstAttention designs a dedicated flashaware in-storage attention engine with KV cache management mechanisms to exploit the high internal bandwidths of CSDs instead of being limited by the PCIe bandwidth. The optimized P2P transmission between GPU and CSDs further reduces data migration overheads. Experimental results demonstrate that for a 13B model using an NVIDIA A6000 GPU, InstAttention improves throughput for long-sequence inference by up to $11.1 \times$, compared to existing SSD-based solutions such as FlexGen.

关键词： Costs Generative AI Large language models Memory management Graphics processing units Bandwidth Throughput Decoding Engines Edge computing

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Reviving In-Storage Hardware Compression on ZNS SSDs through Host-SSD Collaboration

Reviving In-Storage Hardware Compression on ZNS SSDs through...

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ieee symposium on high-performance computer architecture

作者： Yingjia Wang Tao Lu Yuhong Liang Xiang Chen Ming-Chang Yang The Chinese University of Hong Kong DapuStor Corporation

ISBN: (数字)9798331506476

ISBN: (纸本)9798331506483

Zoned Namespace (ZNS) is an emerging SSD interface with great potential for performance and cost in largescale cloud SSD deployments. Enabling in-storage hardware compression on ZNS SSDs is promising for further enhancing the cost-effectiveness of ZNS-based storage infrastructures. However, based on our investigation, existing solutions based on the host-transparent methodology are sub-optimal on ZNS SSDs due to two intrinsic challenges: (1) locating compressed chunks and (2) harvesting space savings from *** this paper, we for the first time revisit the compression storage system architecture on the emerging ZNS SSD and propose to decouple compression execution with indexing. We propose CCZNS (CC: collaborative compression), an advanced ZNS interface that revives in-storage hardware compression on ZNS SSDs through a novel host-SSD collaborative approach. We present how CCZNS can benefit host software by performing a case study on RocksDB and ZenFS. Extensive experiments demonstrate that the CCZNS-based storage system significantly outperforms existing system solutions.

关键词： Costs Storage management Collaboration Systems architecture computer architecture Hardware Software Indexing

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Zebra: Efficient Redundant Array of Zoned Namespace SSDs Enabled by Zone Random Write Area (ZRWA)

Zebra: Efficient Redundant Array of Zoned Namespace SSDs Ena...

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ieee symposium on high-performance computer architecture

作者： Tianyang Jiang Guangyan Zhang Xiaojian Liao Yuqi Zhou Huawei Technologies Co. Ltd. Department of Computer Science and Technology BNRist Tsinghua University Beihang University China University of Geosciences

ISBN: (数字)9798331506476

ISBN: (纸本)9798331506483

Zoned Namespace (ZNS) SSDs have emerged as a promising solution for eliminating device-level garbage collection and achieving predictable performance through the zone abstraction, which supports append-only writes. However, integrating ZNS SSDs into RAID configurations presents challenges, particularly concerning partial parity updates (PPUs) due to the no-overwrite property of ZNS SSDs. Existing solutions introduce dedicated metadata zones to manage PPUs, but these zones become performance bottlenecks, especially under high PPU *** this paper, we introduce Zebra, a novel architecture for ZNS RAID that leverages the Zone Random Write Area (ZRWA) feature of modern ZNS SSDs. Our key insight is that the SSD’s write buffer (or ZRWA) in front of each zone is ideal for PPUs that exhibit a repeated sequential overwrite I/O pattern. By ensuring that the parity chunk fits within the ZRWA window, Zebra enables efficient PPUs within the ZRWA. Combined with a set of techniques, Zebra ensures both high performance and reliability. We evaluate Zebra on both large-zone and small-zone ZNS SSDs, two typical ZNS models. Our experiments with micro and application benchmarks show that Zebra improves the throughput by 1.1×-4.2× compared to RAIZN, a state-of-the-art ZNS RAID system.

关键词： performance evaluation Fault tolerance Fault tolerant systems computer architecture Metadata Benchmark testing Throughput

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EXION: Exploiting Inter-and Intra-Iteration Output Sparsity for Diffusion Models

EXION: Exploiting Inter-and Intra-Iteration Output Sparsity ...

引用

ieee symposium on high-performance computer architecture

作者： Jaehoon Heo Adiwena Putra Jieon Yoon Sungwoong Yune Hangyeol Lee Ji-Hoon Kim Joo-Young Kim KAIST Daejeon Republic of Korea

ISBN: (数字)9798331506476

ISBN: (纸本)9798331506483

Over the past few years, diffusion models have emerged as novel solutions in AI industries, offering the capability to generate diverse, multi-modal outputs such as images, videos, and motions from input prompts (i.e., text). Despite their impressive capabilities, diffusion models face significant challenges in computing, including excessive latency and energy consumption due to the numerous iterations in model architecture. Although prior works specialized in transformer acceleration can be applied to diffusion models, given that transformers are key components, the problem of the iterative nature of diffusion models still needs to be *** this paper, we present EXION, the first software-hardware co-designed diffusion accelerator that solves the computation challenges of excessive iterations by exploiting the unique inter-and intra-iteration output sparsity in diffusion models. To this end, we propose two software-level optimizations in EXION. First, we introduce the FFN-Reuse algorithm that identifies and skips redundant computations in FFN layers across different iterations (i.e., inter-iteration sparsity). Second, we use a modified eager prediction method that employs two-step leading-one detection to accurately predict the attention score in diffusion models, skipping unnecessary computations within an iteration (i.e., intra-iteration sparsity). We also introduce a novel data compaction mechanism named ConMerge, which can enhance hardware utilization by condensing and merging large and sparse matrices into small and compact forms. Finally, EXION has a dedicated hardware architecture that supports the above sparsity-inducing algorithms, translating high output sparsity into improved energy efficiency and performance. To verify the feasibility of the EXION accelerator, we first demonstrate that it has no impact on accuracy in various types of multi-modal diffusion models, including text-to-motion, -audio, -image, and -video. We then instantiate EXION in b

关键词： Graphics processing units computer architecture Prediction methods Diffusion models Transformers Prediction algorithms Energy efficiency Sparse matrices Servers Optimization

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Implementation of AI-Powered Robotic Chatbot for Intelligent Customer Interaction

Implementation of AI-Powered Robotic Chatbot for Intelligent...

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2025 international Conference on Intelligent Control, computing and Communications, IC3 2025

作者： Duraisamy, Premkumar Gokul, C. Jeevanandhan, A. Shirthik Ram, S. Kpr Institute of Engineering and Technology Department of Computer Science and Engineering Coimbatore India Kpr Institute of Engineering and Technology Department of Electrical Communication and Engineering Coimbatore India

ISBN: (纸本)9798331527495

This work presents the implementation of an AI-powered robotic chatbot designed to transform customer service in the online shopping sector. The chatbot is engineered to manage key customer interactions, including order placement, shipment tracking, order modifications, and cancellations, through a seamless conversational interface. Utilizing advanced natural language processing (NLP) models from Hugging Face Transformers and intent recognition via Dialogflow, the system ensures context-aware and precise responses. The integration reduces response times and enhances customer satisfaction. Experimental evaluation demonstrates high accuracy in intent recognition and response generation, validating the chatbot's efficacy as a reliable solution for automating customer service tasks. The paper details the system's architecture, implementation, and performance, highlighting its capability to optimize customer support and boost e-commerce operational efficiency. © 2025 ieee.

关键词： Chatbots

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NOVA: A Novel Vertex Management architecture for Scalable Graph Processing

NOVA: A Novel Vertex Management Architecture for Scalable Gr...

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ieee symposium on high-performance computer architecture

作者： Marjan Fariborz Mahyar Samani Austin York S.J. Ben Yoo Jason Lowe-Power Venkatesh Akella Ayar Labs University of California Davis Google

ISBN: (数字)9798331506476

ISBN: (纸本)9798331506483

We propose a scalable graph processing hardware accelerator called NOVA that is based on a novel vertex management architecture that decouples the execution of reduction and propagation operations in the popular vertex-centric graph processing paradigm. This allows us to store the working set in off-chip memory and utilize the available on-chip memory as a buffer to hide the latency of DRAM accesses instead of a traditional cache. This overcomes one of the key drawbacks of almost all the prior works which require temporal partitioning of graphs to scale to large graphs. We develop a cycle-accurate model of the architecture in gem 5 and demonstrate that NOVA exhibits near-perfect weak and strong scaling while scaling to large graphs by spatially tiling multiple nodes. In addition, our simulations show that NOVA is $2.35 \times$ better than a state-of-the-art graph accelerator (PolyGraph) while using a fraction of the on-chip memory on a synthetic graph with 134M vertices and over 2.14B edges.

关键词： Degradation Random access memory computer architecture Bandwidth Throughput System-on-chip Hardware acceleration

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All-Optical Reconfigurable Activation Function Based on Saturable Absorption

All-Optical Reconfigurable Activation Function Based on Satu...

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ieee international symposium on Quality Electronic Design

作者： Oceane Destras Felipe Gohring De Magalhaes Sébastien Le Beux Gabriela Nicolescu Département de génie informatique et génie logiciel Polytechnique Montreal Montreal QC Canada Department of Electrical and Computer Engineering Concordia University Montreal QC Canada

ISBN: (数字)9798331509422

ISBN: (纸本)9798331509439

The development of optical neural networks (ONNs) has opened up new possibilities for high-speed and energy-efficient computing. However, the limited range of available optical activation functions poses a significant bottleneck. To address this, we propose an all-optical, reconfigurable activation function circuit based on semiconductor saturable absorber mirrors and Mach-Zehnder interferometers, capable of emulating activation functions such as ReLU, Sigmoid, and Tanh. The reprogrammability of the proposed design allows the same circuit to be reused for multiple tasks by dynamically adjusting the activation function, optimizing both space and energy efficiency in ONNs. Through extensive testing, we have identified the most critical parameters of the optical activation function for effective training of ONNs. We demonstrate via numerical simulation that this optical activation function achieves competitive results for the performance of ONNs, reaching up to 98.5% accuracy on the MNIST dataset.

关键词： Integrated optics Training Optical interferometry Optical design Neural networks Optical computing computer architecture Optical fiber networks Nonlinear optics high-speed optical techniques

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