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Utilizing market forecasts is pivotal in optimizing portfolio selection strategies. We introduce DeepClair, a novel framework for portfolio selection. DeepClair leverages a transformer-based time-series forecasting model to predict market trends, ...

Graph neural networks (GNNs) are one of the rapidly growing fields within deep learning. While many distributed GNN training frameworks have been proposed to increase the training throughput, they face three limitations when applied to multi-server ...

Existing Neural Architecture Search algorithms achieve a low error rate in vision tasks, such as image classification, by training child networks with equal resources during the search. However, it is unnecessary to allocate equal resources or ...

Recent advances in adversarial robustness rely on an abundant set of training data, where using external or additional datasets has become a common setting. However, in real life, the training data is often kept private for security and privacy issues, ...

With the advance in genome sequencing technology, the lengths of deoxyribonucleic acid (DNA) sequencing results are rapidly increasing at lower prices than ever. However, the longer lengths come at the cost of a heavy computational burden on aligning ...

Because of the widely recognized memory wall issue, modern DRAMs are increasingly being assigned innovative functionalities beyond the basic read and write operations. Often referred to as “function-in-memory”, these techniques are crafted ...

Conditional Generative Adversarial Networks (CGANs) are well developed on balanced datasets as default standards for generating high-quality images of expected classes. However, the common problem in practice is that training datasets are ...

Line drawing is a form of painting that uses lines as expressive elements and often employs the combination of abstraction and figuration (CAF) technique to enhance artistic expression. However, existing methods tend to focus on generating semantically ...

Neural Processing Units (NPUs) frequently suffer from low hardware utilization as the efficiency of their systolic arrays heavily depends on the characteristics of a deep neural network (DNN). Spatial multitasking is a promising solution to overcome the ...

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, ...

In training of modern large natural language processing (NLP) models, it has become a common practice to split models using 3D parallelism to multiple GPUs. Such technique, however, suffers from a high overhead of inter-node communication. Compressing ...

As more and more mobile/embedded applications employ Deep Neural Networks (DNNs) involving sensitive user data, mobile/embedded devices must provide a highly secure DNN execution environment to prevent privacy leaks. Aimed at securing DNN data, recent ...

Graph convolutional networks (GCNs) are becoming increasingly popular as they can process a wide variety of data formats that prior deep neural networks cannot easily support. One key challenge in designing hardware accelerators for GCNs is the vast ...

Only with a right schedule and a right topology layout, a graph algorithm can be efficiently processed on GPUs. Existing GPU graph processing frameworks try to find an optimal schedule and topology layout for an algorithm via iterative search, but they ...

Co-exploration of an optimal neural architecture and its hardware accelerator is an approach of rising interest which addresses the computational cost problem, especially in low-profile systems. The large co-exploration space is often handled by adopting ...

Analytical models can greatly help computer architects perform orders of magnitude faster early-stage design space exploration than using cycle-level simulators. To facilitate rapid design space exploration for graphics processing units (GPUs), prior ...

The fast, efficient processing of graphs is needed to quickly analyze and understand connected data, from large social network graphs, to edge devices performing timely, local data analytics. But, as graph data tends to exhibit poor locality, designing ...

Model quantization is known as a promising method to compress deep neural networks, especially for inferences on lightweight mobile or edge devices. However, model quantization usually requires access to the original training data to maintain the ...

We present dataflow mirroring, architectural support for low-overhead fine-grained systolic array allocation which overcomes the limitations of prior coarse-grained spatial-multitasking Neural Processing Unit (NPU) architectures. The key idea of dataflow ...

Coarse-Grained Reconfigurable Arrays (CGRAs) can offer both energy-efficiency and high-throughput for embedded systems today. But, one limitation of CGRAs is the extremely long mapping time that can take many hours to complete for a typical workload. This ...