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A full spectrum of computing-in-memory technologies
Computing in memory (CIM) could be used to overcome the von Neumann bottleneck and to
provide sustainable improvements in computing throughput and energy efficiency …
provide sustainable improvements in computing throughput and energy efficiency …
[HTML][HTML] A Comprehensive Review of Processing-in-Memory Architectures for Deep Neural Networks
This comprehensive review explores the advancements in processing-in-memory (PIM)
techniques and chiplet-based architectures for deep neural networks (DNNs). It addresses …
techniques and chiplet-based architectures for deep neural networks (DNNs). It addresses …
Neupims: Npu-pim heterogeneous acceleration for batched llm inferencing
Modern transformer-based Large Language Models (LLMs) are constructed with a series of
decoder blocks. Each block comprises three key components:(1) QKV generation,(2) multi …
decoder blocks. Each block comprises three key components:(1) QKV generation,(2) multi …
Functionally-complete boolean logic in real dram chips: Experimental characterization and analysis
Processing-using-DRAM (PuD) is an emerging paradigm that leverages the analog
operational properties of DRAM circuitry to enable massively parallel in-DRAM computation …
operational properties of DRAM circuitry to enable massively parallel in-DRAM computation …
MIMDRAM: An end-to-end processing-using-DRAM system for high-throughput, energy-efficient and programmer-transparent multiple-instruction multiple-data …
Processing-using-DRAM (PUD) is a processing-in-memory (PIM) approach that uses a
DRAM array's massive internal parallelism to execute very-wide (eg, 16,384-262,144-bit …
DRAM array's massive internal parallelism to execute very-wide (eg, 16,384-262,144-bit …
Pathfinding future pim architectures by demystifying a commercial pim technology
Processing-in-memory (PIM) has been explored for decades by computer architects, yet it
has never seen the light of day in real-world products due to its high design overheads and …
has never seen the light of day in real-world products due to its high design overheads and …
pLUTo: Enabling massively parallel computation in DRAM via lookup tables
Data movement between the main memory and the processor is a key contributor to
execution time and energy consumption in memory-intensive applications. This data …
execution time and energy consumption in memory-intensive applications. This data …
Swiftrl: Towards efficient reinforcement learning on real processing-in-memory systems
Reinforcement Learning (RL) is the process by which an agent learns optimal behavior
through interactions with experience datasets, all of which aim to maximize the reward …
through interactions with experience datasets, all of which aim to maximize the reward …
Evaluating homomorphic operations on a real-world processing-in-memory system
Computing on encrypted data is a promising approach to reduce data security and privacy
risks, with homomorphic encryption serving as a facilitator in achieving this goal. In this work …
risks, with homomorphic encryption serving as a facilitator in achieving this goal. In this work …
PULSAR: Simultaneous many-row activation for reliable and high-performance computing in off-the-shelf DRAM chips
Data movement between the processor and the main memory is a first-order obstacle
against improving performance and energy efficiency in modern systems. To address this …
against improving performance and energy efficiency in modern systems. To address this …