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A survey of machine learning for computer architecture and systems
It has been a long time that computer architecture and systems are optimized for efficient
execution of machine learning (ML) models. Now, it is time to reconsider the relationship …
execution of machine learning (ML) models. Now, it is time to reconsider the relationship …
Pond: Cxl-based memory pooling systems for cloud platforms
H Li, DS Berger, L Hsu, D Ernst, P Zardoshti… - Proceedings of the 28th …, 2023 - dl.acm.org
Public cloud providers seek to meet stringent performance requirements and low hardware
cost. A key driver of performance and cost is main memory. Memory pooling promises to …
cost. A key driver of performance and cost is main memory. Memory pooling promises to …
Online metric algorithms with untrusted predictions
A Antoniadis, C Coester, M Eliáš, A Polak… - ACM transactions on …, 2023 - dl.acm.org
Machine-learned predictors, although achieving very good results for inputs resembling
training data, cannot possibly provide perfect predictions in all situations. Still, decision …
training data, cannot possibly provide perfect predictions in all situations. Still, decision …
Pythia: A customizable hardware prefetching framework using online reinforcement learning
R Bera, K Kanellopoulos, A Nori, T Shahroodi… - MICRO-54: 54th Annual …, 2021 - dl.acm.org
Past research has proposed numerous hardware prefetching techniques, most of which rely
on exploiting one specific type of program context information (eg, program counter …
on exploiting one specific type of program context information (eg, program counter …
The championship simulator: Architectural simulation for education and competition
Recent years have seen a dramatic increase in the microarchitectural complexity of
processors. This increase in complexity presents a twofold challenge for the field of …
processors. This increase in complexity presents a twofold challenge for the field of …
The dawn of ai-native eda: Opportunities and challenges of large circuit models
Within the Electronic Design Automation (EDA) domain, AI-driven solutions have emerged
as formidable tools, yet they typically augment rather than redefine existing methodologies …
as formidable tools, yet they typically augment rather than redefine existing methodologies …
An imitation learning approach for cache replacement
Program execution speed critically depends on increasing cache hits, as cache hits are
orders of magnitude faster than misses. To increase cache hits, we focus on the problem of …
orders of magnitude faster than misses. To increase cache hits, we focus on the problem of …
A hierarchical neural model of data prefetching
This paper presents Voyager, a novel neural network for data prefetching. Unlike previous
neural models for prefetching, which are limited to learning delta correlations, our model can …
neural models for prefetching, which are limited to learning delta correlations, our model can …
The case for distributed shared-memory databases with RDMA-enabled memory disaggregation
Memory disaggregation (MD) allows for scalable and elastic data center design by
separating compute (CPU) from memory. With MD, compute and memory are no longer …
separating compute (CPU) from memory. With MD, compute and memory are no longer …
Baleen:{ML} admission & prefetching for flash caches
DLK Wong, H Wu, C Molder, S Gunasekar… - … USENIX Conference on …, 2024 - usenix.org
Flash caches are used to reduce peak backend load for throughput-constrained data center
services, reducing the total number of backend servers required. Bulk storage systems are a …
services, reducing the total number of backend servers required. Bulk storage systems are a …