Search Results

Recent advances in Dual In-line Memory Modules (DIMMs) allow DIMMs to support Processing-In-DIMM (PID) by placing In-DIMM Processors (IDPs) near their memory banks. Prior studies have shown that in-memory joins can benefit from PID by offloading their ...

Graph Neural Networks (GNNs) are emerging models to analyze graph-structure data. GNN execution involves both compute-intensive and memory-intensive kernels. The latter kernels dominate execution time, because they are significantly bottlenecked by data ...

Graph processing has become a central concern for many real-world applications and is well-known for its low compute-to-communication ratios and poor data locality. By integrating computing logic into memory, resistive random access memory (ReRAM) tackles ...

In-SRAM computing promises energy efficiency, but circuit nonlinearities and PVT variations pose major challenges in designing robust accelerators. To address this, we introduce OPTIMA, a modeling framework that aids in analyzing bitline discharge and ...

Analog in-memory-computing (IMC) is an attractive technique with a higher energy efficiency to process machine learning workloads. However, the analog computing scheme suffers from large interface circuit overhead. In this work, we propose a macro with a ...

Solving sparse linear systems is crucial in scientific computing. Sparse Conjugate Gradient (CG) is one of the most well-known iterative solvers with high efficiency and low storage requirements. However, the performance of sparse CG solvers implemented ...

k-Clique counting problem plays an important role in graph mining which has seen a growing number of applications. However, current k-Clique counting accelerators cannot meet the performance requirement mainly because they struggle with high data ...

Regular path queries (RPQs) in graph databases are bottlenecked by the memory wall. Emerging processing-in-memory (PIM) technologies offer a promising solution to dispatch and execute path matching tasks in parallel within PIM modules. We present ...

Graph processing is widely used for many modern applications, such as social networks, recommendation systems, and knowledge graphs. However, processing large-scale graphs on traditional Von Neumann architectures is challenging due to the irregular graph ...

Processing-in-memory (PIM) has been widely explored in academia and industry to accelerate numerous workloads. By reducing the data movement and increasing parallelism, PIM offers great performance and energy efficiency. A large amount of cores or nodes ...

Accelerating end-to-end inference of transformer-based large language models (LLMs) is a critical component of AI services in datacenters. However, the diverse compute characteristics of LLMs' end-to-end inference present challenges as previously ...

Processing-In-Memory (PIM) is a novel approach that augments existing DRAM memory chips with lightweight logic. By allowing to offload computations to the PIM system, this architecture allows for circumventing the data-bottleneck problem that affects ...

The Transformer-based generative model (TbGM), comprising summarization (Sum) and generation (Gen) stages, has demonstrated unprecedented generative performance across a wide range of applications. However, it also demands immense amounts of compute and ...

Processing-in-memory (PIM), where the compute is moved closer to the memory or the data, has been widely explored to accelerate emerging workloads. Recently, different PIM-based systems have been announced by memory vendors to minimize data movement and ...

While vector architecture is widely employed in processors for neural networks, signal processing, and high-performance computing; however, its performance is limited by inefficient column-major memory access. The column-major access limitation originates ...

Modern recommendation systems are widely used in modern data centers. The random and sparse embedding lookup operations are the main performance bottleneck for processing recommendation systems on traditional platforms as they induce abundant data ...

Resistive random access memory (ReRAM) is a promising technology that can perform low-cost and in-situ matrix-vector multiplication (MVM) in analog domain. Scientific computing requires high-precision floating-point (FP) processing. However, performing ...

Processing-in-memory (PIM) is an emerging technology to alleviate the high cost of data movement by pushing computation into/near memory modules. There is an inherent tension, however, between minimizing communication (data movement) and achieving load ...

Modern dual in-line memory modules (DIMMs) support processing-in-memory (PIM) by implementing in-DIMM processors (IDPs) located near memory banks. PIM can greatly accelerate in-memory join, whose performance is frequently bounded by main-memory accesses, ...

Modern out-of-order processors call for more aggressive scheduling techniques such as priority scheduling and out-of-order commit to make use of increasing core resources. Since these approaches prioritize the issue or commit of certain instructions, ...