Hardware accelerator design for data centers

Yesil, Serif; Özdal, Mustafa; Ayupov, Andrey; Burns, Steven; Öztürk, Özcan

doi:10.1109/iccad.2015.7372648

Cited by 6 publications

(2 citation statements)

References 36 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…The cloud service providers (CSP) have moved away from CPU-centric monolithic design where CPU, memory, and devices are on the same physical platform [1]. Instead, domainspecific accelerators or DSAs have become an essential part of modern data centers [2][3][4] for workloads such as AI/ML [5,6], graph processing [7,8], in-memory cache [9,10], and many more. DSAs are often deployed as standalone nodes in a rack with network functions [11] to communicate with other nodes [12,13].…”

Section: Introductionmentioning

confidence: 99%

Empowering Data Centers for Next Generation Trusted Computing

Dhar¹,

Sridhara²,

Shinde³

et al. 2022

Preprint

View full text Add to dashboard Cite

Modern data centers have grown beyond CPU nodes to provide domain-specific accelerators such as GPUs and FP-GAs to their customers. From a security standpoint, cloud customers want to protect their data. They are willing to pay additional costs for trusted execution environments such as enclaves provided by Intel SGX and AMD SEV. Unfortunately, the customers have to make a critical choice-either use domain-specific accelerators for speed or use CPU-based confidential computing solutions.To bridge this gap, we aim to enable data-center scale confidential computing that expands across CPUs and accelerators. We argue that having wide-scale TEE-support for accelerators presents a technically easier solution, but is far away from being a reality. Instead, our hybrid design provides enclaved execution guarantees for computation distributed over multiple CPU nodes and devices with/without TEE support. Our solution scales gracefully in two dimensions-it can handle a large number of heterogeneous nodes and it can accommodate TEE-enabled devices as and when they are available in the future. We observe marginal overheads of 0.42-8% on real-world AI data center workloads that are independent of the number of nodes in the data center. We add custom TEE support to two accelerators (AI and storage) and integrate it into our solution, thus demonstrating that it can cater to future TEE devices.

show abstract

Section: Introductionmentioning

confidence: 99%

Empowering Data Centers for Next Generation Trusted Computing

Dhar¹,

Sridhara²,

Shinde³

et al. 2022

Preprint

View full text Add to dashboard Cite

show abstract

“…Breakthrough streaming applications such as virtual reality, augmented reality, autonomous vehicles, and multimedia that demand for high-performance and power-efficient computing necessitate the use of Application specific ACCelerators (ACCs). ACCs have come into the scene to provide orders of magnitude enhancement in both power and performance efficient computing over massive amount of data as compared to general purpose designs [123].…”

Section: Accelerator-based Cmp (Acmp)mentioning

confidence: 99%