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Research Manuscript
:
Active Memory: Breaking the Data Movement Wall with Cognitive Storage
DescriptionThis session heralds the dawn of 'Active Memory', where the boundary between storage and computation dissolves. By embedding intelligence -- from fine-tuned LLMs to in-storage Hyperdimensional Computing -- directly into the hardware fabric, these works tackle the 'Data Movement Wall' head-on. Spanning Mixture-of-Experts (MoE) acceleration, Ethereum analytics, and MRAM-based BNNs, this session redefines memory as a cognitive engine. It marks a paradigm shift toward autonomous, energy-efficient silicon capable of processing massive AI workloads at the source, unlocking performance for next-generation intelligent systems.
Event Type
Research Manuscript
TimeWednesday, July 293:30pm - 5:30pm PDT
LocationMtg Room 203C
Topics
Systems
Tracks
SYS5. Embedded Memory and Storage Systems
Presentations
3:30pm - 3:42pm PDTBSGCN: Taming Diverse Sparsities in GCNs via Adaptive Band-Segmentation and Tailored Caching
3:42pm - 3:54pm PDTKnowledge-Driven Hybrid SSD Management Enhanced by Fine-Tuned LLMs
3:54pm - 4:06pm PDTSpecANNS: Accelerating Graph-Based Approximate Nearest Neighbor Search with Speculative In-Storage Computing
4:06pm - 4:18pm PDTVALVE: Accelerating Visible Version Searching in HTAP Systems via In-Storage Computing
4:18pm - 4:30pm PDTFlashhd: A Flash-Based In-Storage Hyperdimensional Computing Framework for Hierarchical Sequence Matching
4:30pm - 4:42pm PDTExpcheck: Dynamic Expert-Aware Checkpointing for Mixture-of-Experts Based Models
4:42pm - 4:54pm PDTEtherssd: An In-Storage Ethereum Analytics Platform with Minimized I/O and Authentication Overhead
4:54pm - 5:06pm PDTNash: A Neighbor-Aware Shared Memory Design on GPU for Accelerating AI Workloads
5:06pm - 5:18pm PDTCOM-BNN: A Configurable Low Power MRAM-Based Computing-in-Memory Accelerator for Binary Convolutional Computations
5:18pm - 5:30pm PDTOnyx: Efficient Transaction Processing with Real Processing-in-Memory Prototypes