HPCA 2025

Meta Info

Homepage: https://hpca-conf.org/2025/

Paper list: https://hpca-conf.org/2025/main-program/

Acceptance Rate

21% (= 112 / 534)

Papers

Large Language Models (LLMs)

• LLM Compression

  • eDKM: An Efficient and Accurate Train-Time Weight Clustering for Large Language Models [arXiv]

    • Apple

    • Compress LLMs to fit into storage-limited devices.

    • Propose a memory-efficient DKM (Differentiable KMeans Clustering) implementation.

    • Fine-tune and compress a pretrained LLaMA 7B model from 12.6 GB to 2.5 GB.

• LLM Quantization

  • BitMoD: Bit-serial Mixture-of-Datatype LLM Acceleration [arXiv] [Code]

    • Cornell & MSR & ICL

    • Algorithm: Fine-grained data type adaptation that uses a different numerical data type to quantize a group of (e.g., 128) weights.

    • Hardware: Employ a bit-serial processing element to support multiple numerical precisions and data types.

  • MANT: Efficient Low-bit Group Quantization for LLMs via Mathematically Adaptive Numerical Type [arXiv]

    • SJTU

    • Assign the appropriate data type for each group adaptively.

    • Propose an efficient real-time quantization mechanism.

    • Implement a specific processing element to efficiently support MANT and incorporate a real-time quantization unit.

  • Anda: Unlocking Efficient LLM Inference with a Variable-Length Grouped Activation Data Format [arXiv]

    • NJU & MICAS KU Leuven

    • Investigate the sensitivity of activation precision across various LLM modules and its impact on overall model accuracy.

    • Anda data type: an adaptive data format with group-shared exponent bits and dynamic mantissa bit allocation.

  • VQ-LLM: High-performance Code Generation for Vector Quantization Augmented LLM Inference

    • SJTU

• Energy-Efficient LLM Inference

  • DynamoLLM: Designing LLM Inference Clusters for Performance and Energy Efficiency [arXiv]

    • UIUC & Microsoft Azure

    • Given the current load and available resources, select the energy-optimized configuration (e.g., different model parallelisms, GPU frequencies).

  • throttLL’eM: Predictive GPU Throttling for Energy Efficient LLM Inference Serving

    • National Technical University of Athens

• Long-Context LLM Inference

  • InstAttention: In-Storage Attention Offloading for Cost-Effective Long-Context LLM Inference [arXiv]

    • PKU

    • Offload the attention in decoding phase KV cache to Computational Storage Drives (CSDs).

• Hardware-Assisted LLM Inference

  • LAD: Efficient Accelerator for Generative Inference of LLM with Locality Aware Decoding

    • ICT, CAS

  • PAISE: PIM-Accelerated Inference Scheduling Engine for Transformer-based LLM

    • Samsung SDS

  • FACIL: Flexible DRAM Address Mapping for SoC-PIM Cooperative On-device LLM Inference

    • Seoul National University

  • Lincoln: Real-Time 50~100B LLM Inference on Consumer Devices with LPDDR-Interfaced, Compute-Enabled Flash Memory

    • THU

  • Make LLM Inference Affordable to Everyone: Augmenting GPU Memory with NDP-DIMM [arXiv]

    • ICT, CAS

    • Hermes

Diffusion Models

• EXION: Exploiting Inter- and Intra-Iteration Output Sparsity for Diffusion Models [arXiv]

  • KAIST

  • Inter-iteration sparsity → a FFN-Reuse algorithm to identify and skip redundant computations in FFN layers across different iterations

  • Intra-iteration sparsity → A modified eager prediction method to accurately predict the attention score, skipping unnecessary computations within an iteration

  • A dedicated hardware architecture to support the sparsity-inducing algorithms.

• Ditto: Accelerating Diffusion Model via Temporal Value Similarity [arXiv]

  • Yonsei University

  • Ditto: a difference processing algorithm

    • Leverage temporal similarity with quantization.

    • Perform full bit-width operations for the initial time step and process subsequent steps with temporal differences.

  • Design the Ditto hardware → a specialized hardware accelerator

Deep Learning Recommendation Models (DLRMs)

• Machine Learning-Guided Memory Optimization for DLRM Inference on Tiered Memory

  • UC Merced & Meta

Dynamic Neural Networks

• Adyna: Accelerating Dynamic Neural Networks with Adaptive Scheduling

  • THU

ML Cluster Reliability

• Enhancing Large-Scale AI Training Efficiency: The C4 Solution for Real-Time Anomaly Detection and Communication Optimization [arXiv]

  • Alibaba

• Revisiting Reliability in Large-Scale Machine Learning Research Clusters [arXiv]

  • Meta

ML for Systems

• The Importance of Generalizability in Machine Learning for Systems

  • MIT & Google

ML Benchmark

• MLPerf Power: Benchmarking the Energy Efficiency of Machine Learning Systems from μWatts to MWatts for Sustainable AI [arXiv]

Multi-GPU Systems

• OASIS: Object-Aware Page Management for Multi-GPU Systems [Paper]

  • Pittsburgh & NVIDIA & Ghent

Collective Communication

• TidalMesh: Topology-Driven AllReduce Collective Communication for Mesh Topology

  • KAIST

• PIMnet: A Domain-Specific Network for Efficient Collective Communication in Scalable PIM

  • KAIST & Northeastern University & Boston University

Interconnect

• Push Multicast: A Speculative and Coherent Interconnect for Mitigating Manycore CPU Communication Bottleneck [Code]

  • HKUST-GZ & Intel & UCSD & HKUST

• EIGEN: Enabling Efficient 3DIC Interconnect with Heterogeneous Dual-Layer Network-on-Active-Interposer

  • FDU

Compute Express Link (CXL)

• SkyByte: Architecting An Efficient Memory-Semantic CXL-based SSD with OS and Hardware Co-design [arXiv]

  • UIUC

  • Opportunistic context switches upon the detection of long access delays.

  • Data coalescing upon log cleaning to reduce the I/O traffic to flash chips.

  • Adaptive page migration to promote hot pages in CXL-SSD to the host.

  • Implemented with a CXL-SSD simulator.

Near-Memory Processing

• AsyncDIMM: Achieving Asynchronous Execution in DIMM-Based Near-Memory Processing

  • SJTU

• UniNDP: A Unified Compilation and Simulation Tool for Near DRAM Processing Architectures [Code]

  • THU & HKUST & PKU

Bandwidth Partitioning

• Criticality-Aware Instruction-Centric Bandwidth Partitioning for Data Center Applications

  • PKU

Deep Learning Accelerator

• LUT-DLA: Lookup Table as Efficient Extreme Low-Bit Deep Learning Accelerator [arXiv]

  • MSRA & NTU

  • Transform various DNN models into LUTs (Look-Up Tables) via multistage training to achieve extreme low-bit quantization.

• FIGLUT: An Energy-Efficient Accelerator Design for FP-INT GEMM Using Look-Up Tables

  • POSTECH & NAVER

Image Signal Processor

• IRIS: Unleashing ISP-Software Cooperation to Optimize the Machine Vision Pipeline

  • Universitat Politècnica de Catalunya

  • Enable the Image Signal Processor (ISP) to create compressed, mixed-resolution images in real time.

  • Used for image classification & monocular Simultaneous Localization and Mapping (SLAM).

Microservice

• Grad: Intelligent Microservice Scaling by Harnessing Resource Fungibility

  • University of Macau

  • Profile individual microservice latency in relation to environmental conditions.

  • Dynamically select the optimal set of microservices for scaling.

  • An end-to-end latency predictor serves as a simulator to obtain real-time feedback.

Acronyms

• ML: Machine Learning

• DKM: Differentiable KMeans Clustering

• CXL: Compute Express Link

• CSD: Computational Storage Drive

• LUT: Look-Up Table

• ISP: Image Signal Processor

• SLAM: Simultaneous Localization and Mapping