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Dfx: A low-latency multi-fpga appliance for accelerating transformer-based text generation
Transformer is a deep learning language model widely used for natural language
processing (NLP) services in datacenters. Among transformer models, Generative …
processing (NLP) services in datacenters. Among transformer models, Generative …
{CXL-ANNS}:{Software-Hardware} collaborative memory disaggregation and computation for {Billion-Scale} approximate nearest neighbor search
J Jang, H Choi, H Bae, S Lee, M Kwon… - 2023 USENIX Annual …, 2023 - usenix.org
We propose CXL-ANNS, a software-hardware collaborative approach to enable highly
scalable approximate nearest neighbor search (ANNS) services. To this end, we first …
scalable approximate nearest neighbor search (ANNS) services. To this end, we first …
Mtia: First generation silicon targeting meta's recommendation systems
Meta has traditionally relied on using CPU-based servers for running inference workloads,
specifically Deep Learning Recommendation Models (DLRM), but the increasing compute …
specifically Deep Learning Recommendation Models (DLRM), but the increasing compute …
Magma: An optimization framework for map** multiple dnns on multiple accelerator cores
As Deep Learning continues to drive a variety of applications in edge and cloud data
centers, there is a growing trend towards building large accelerators with several sub …
centers, there is a growing trend towards building large accelerators with several sub …
Pathfinding Future PIM Architectures by Demystifying a Commercial PIM Technology
Processing-in-memory (PIM) has been explored for decades by computer architects, yet it
has never seen the light of day in real-world products due to its high design overheads and …
has never seen the light of day in real-world products due to its high design overheads and …
Hercules: Heterogeneity-aware inference serving for at-scale personalized recommendation
Personalized recommendation is an important class of deep-learning applications that
powers a large collection of internet services and consumes a considerable amount of …
powers a large collection of internet services and consumes a considerable amount of …
Scalability Limitations of Processing-in-Memory using Real System Evaluations
G Jonatan, H Cho, H Son, X Wu, N Livesay… - Proceedings of the …, 2024 - dl.acm.org
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 …
has been widely explored to accelerate emerging workloads. Recently, different PIM-based …
Accelerating ML recommendation with over a thousand RISC-V/tensor processors on Esperanto's ET-SoC-1 chip
D Ditzel, R Espasa, N Aymerich, A Baum… - 2021 IEEE Hot Chips …, 2021 - ieeexplore.ieee.org
The ET-SoC-1 has over a thousand RISC-V processors on a single TSMC 7nm chip,
including:• 1088 energy-efficient ET-Minion 64-bit RISC-V in-order cores each with a …
including:• 1088 energy-efficient ET-Minion 64-bit RISC-V in-order cores each with a …
Special session: Towards an agile design methodology for efficient, reliable, and secure ML systems
The real-world use cases of Machine Learning (ML) have exploded over the past few years.
However, the current computing infrastructure is insufficient to support all real-world …
However, the current computing infrastructure is insufficient to support all real-world …
The Landscape of Compute-near-memory and Compute-in-memory: A Research and Commercial Overview
In today's data-centric world, where data fuels numerous application domains, with machine
learning at the forefront, handling the enormous volume of data efficiently in terms of time …
learning at the forefront, handling the enormous volume of data efficiently in terms of time …