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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 ...

Fine-tuning is the gateway to transferring learned knowledge in a pre-trained Large Language Model (LLM) on many downstream applications. To make LLM fine-tuning more affordable, prior works follow two paths: i) adapters freeze the pre-trained LLM ...

Spike sorting is the process of parsing electrophysiological signals from neurons to identify if, when, and which particular neurons fire. Spike sorting is a particularly difficult task in computational neuroscience due to the growing scale of recording ...

Atalanta is a lossless, hardware/software co-designed compression technique for the tensors of fixed-point quantized deep neural networks. Atalanta increases effective memory capacity, reduces off-die traffic, and/or helps to achieve the desired ...

Sparse Convolution (SC) is widely used for processing 3D point clouds that are inherently sparse. Different from dense convolution, SC preserves the sparsity of the input point cloud by only allowing outputs to specific locations. To efficiently compute ...

Traditional data centers include monolithic servers that tightly integrate CPU, memory and disk (Figure 1a). Instead, Disaggregated Systems (DSs) [8, 13, 18, 27] organize multiple compute (CC), memory (MC) and storage devices as independent, failure-...

Resource disaggregation offers a cost effective solution to resource scaling, utilization, and failure-handling in data centers by physically separating hardware devices in a server. Servers are architected as pools of processor, memory, and storage ...

Graph coloring is widely used to parallelize scientific applications by identifying subsets of independent tasks that can be executed simultaneously. Graph coloring assigns colors the vertices of a graph, such that no adjacent vertices have the ...$$$$

Several manufacturers have already started to commercialize near-bank Processing-In-Memory (PIM) architectures, after decades of research efforts. Near-bank PIM architectures place simple cores close to DRAM banks. Recent research demonstrates that they ...

Several manufacturers have already started to commercialize near-bank Processing-In-Memory (PIM) architectures, after decades of research efforts. Near-bank PIM architectures place simple cores close to DRAM banks. Recent research demonstrates that they ...

Important workloads, such as machine learning and graph analytics applications, heavily involve sparse linear algebra operations. These operations use sparse matrix compression as an effective means to avoid storing zeros and performing unnecessary ...

Designing scalable concurrent priority queues for contemporary NUMA servers is challenging. Several NUMA-unaware implementations can scale up to a high number of threads exploiting the potential parallelism of the insert operations. In contrast, in ...