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Industrial recommendation models typically involve numerous feature fields. The embedding computation workloads are heterogeneous across these fields, thus requiring varied optimal code schedules. While existing solutions apply basic fusion optimization ...

The deployment of Deep Learning Recommendation Models (DLRMs) involves the parallelization of extra-large embedding tables (EMTs) on multiple GPUs. Existing works overlook the input-dependent behavior of EMTs and parallelize them in a coarse-grained ...

The photonic platform holds great promise for quantum computing. Nevertheless, the intrinsic probabilistic characteristic of its native fusion operations introduces substantial randomness into the computing process, posing significant challenges to ...

As deep learning models become increasingly complex, the deep learning compilers are critical for enhancing the system efficiency and unlocking hidden optimization opportunities. Although excellent speedups have been achieved in inference workloads, ...

Ensuring high-quality recommendations for newly onboarded users requires the continuous retraining of Deep Learning Recommendation Models (DLRMs) with freshly generated data. To serve the online DLRM retraining, existing solutions use hundreds of CPU ...

Chiplet architecture is an emerging architecture for quantum computing that could significantly increase qubit resources with its great scalability and modularity. However, as the computing scale increases, communication between qubits would become a ...

Zero knowledge Neural Networks draw increasing attention for guaranteeing computation integrity and privacy of neural networks (NNs) based on zero-knowledge Succinct Non-interactive ARgument of Knowledge (zkSNARK) security scheme. However, the ...

The success of a quantum algorithm hinges on the ability to orchestrate a successful application induction. Detrimental overheads in mapping general quantum circuits to physically implementable routines can be the deciding factor between a successful ...

This paper proposes RM-STC, a novel GPU tensor core architecture designed for sparse Deep Neural Networks (DNNs) with two key innovations: (1) native support for both training and inference and (2) high efficiency for all sparsity degrees. To achieve the ...

Distributed quantum computing (DQC) is a scalable way to build a large-scale quantum computing system. Previous compilers for DQC focus on either qubit-to-qubit inter-node gates or qubit-to-node nonlocal circuit blocks, missing opportunities of ...

With the growing number of data-intensive workloads, GPU, which is the state-of-the-art single-instruction-multiple-thread (SIMT) processor, is hindered by the memory bandwidth wall. To alleviate this bottleneck, previously proposed 3D-stacking near-bank ...

With the rapid growth of classification scale in deep learning systems, the final classification layer becomes extreme classification with a memory footprint exceeding the main memory capacity of the CPU or GPU. The emerging in-storage-computing ...

In this paper, we propose OneQ, the first optimizing compilation framework for one-way quantum computation towards realistic photonic quantum architectures. Unlike previous compilation efforts for solid-state qubit technologies, our innovative ...

Quantum computing technology has grown rapidly in recent years, with new technologies being explored, error rates being reduced, and quantum processors' qubit capacity growing. However, near-term quantum algorithms are still unable to be induced ...

Deep neural networks (DNNs) are widely used for computer vision tasks. However, it has been shown that deep models are vulnerable to adversarial attacks—that is, their performances drop when imperceptible perturbations are made to the original inputs, ...

Many relational data in our daily life are represented as graphs, making graph application an important workload. Because of the large scale of graph datasets, moving graph data to the cloud becomes a popular option. To keep the confidential and private ...

Transformers are becoming the mainstream solutions for various tasks like NLP and Computer vision. Despite their success, the high complexity of the attention mechanism hinders them from being applied to latency-sensitive tasks. One opportunity to ...

The dynamic membrane potential threshold, as one of the essential properties of a biological neuron, is a spontaneous regulation mechanism that maintains neuronal homeostasis, i.e., the constant overall spiking firing rate of a neuron. As such, the ...

Deep learning Recommendation Models (DLRMs) plays an important role in various application domains. However, existing DLRM training systems require a large number of GPUs due to the memory-intensive embedding tables. To this end, we propose EL-Rec, an ...

Transformer-based neural models are used in many AI applications. Training these models is expensive, as it takes huge GPU resources and long duration. It is challenging because typical data like sentences have variable lengths, and Transformer's ...