by Helen Wright, Jeff Dean, Mark D. Hill, and Dave Patterson on May 4, 2026 | Tags: AI, Computer Systems
Editor’s Note: this post is a republication of CRA-I post available at: https://cra.org/industry/2026/04/27/how-ai-will-reshape-computer-systems-by-2035-a-jeffersonian-dinner-in-san-francisco-about-our-10000x-future/ CRA-Industry (CRA-I) recently continued its...
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by Digvijay Singh on Apr 29, 2026 | Tags: Data Prefetcher, Memory Wall
This article continues (and concludes) the discussion on the proceedings of DPC-4, covering the remaining four contestants and a summary of the trends observed in all eight prefetchers presented in the championship. Similar to Part I, we focus on how each prefetch...
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by Digvijay Singh on Apr 27, 2026 | Tags: Data Prefetcher, Memory Wall
This article is the first in a two-part series that summarizes the key contributions of 4th Data Prefetching Championship (DPC-4), held in conjunction with the 32nd iteration of HPCA in 2026. While discussing innovative data prefetching techniques presented in this...
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by Yuan Liu, Zihan Chen, Shubdeep Mohapatra, Jim Furches, Zheng (Eddy) Zhang, Huiyang Zhou on Apr 20, 2026 | Tags: Quantum Computing
Hybrid continuous-discrete-variable (CV-DV) quantum computing combines oscillators and qubits to tackle problems that are difficult for either model alone, from bosonic simulation to quantum error correction. At ASPLOS 2026, our tutorial introduced the foundations,...
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by Karu Sankaralingam on Apr 10, 2026 | Tags: Architecture, Evaluation, Machine Learning
For decades, we have designed chips in fundamentally the same way: human intuition applied to a vanishingly small slice of an impossibly large design space. That paradigm worked when Moore’s Law was lifting everything. We could afford to be wrong. We could...
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by Adnan Rakin on Apr 6, 2026 | Tags: deep neural networks, Security, side-channels
Years ago, I came across three pioneering works (CSI-NN, Cache Telepathy, and DeepSniffer) in the field of reverse engineering neural networks that inspired my journey into side-channel attacks to uncover the secrets of modern Deep Neural Networks (DNNs). Fast forward...
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by Sai Srivatsa Bhamidipati on Mar 12, 2026 | Tags: Accelerators, deep neural networks, Machine Learning
The debate of sparsity versus quantization has made its rounds in the ML optimization community for many years. Now, with the Generative AI revolution, the debate is intensifying. While these might both seem like simple mathematical approximations to an AI researcher,...
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by Dmitry Ponomarev on Feb 3, 2026 | Tags: Blog, Editorial
As we close the book on 2025, Computer Architecture Today has seen another successful year of community engagement. We published 29 posts covering a wide spectrum of topics—from datacenter energy-efficiency to the evolving debate on LLMs in peer review, alongside trip...
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by Zhongming Yu and Jishen Zhao on Jan 20, 2026 | Tags: Agents, LLM, Memory Consistency, Memory Hierarchy
Large language model (LLM) agents are quickly moving from “single agent” to *multi-agent systems*: tool-using agents, planner-orchestrator, debate teams, specialized sub-agents that collaborate to solve tasks. At the same time, the *context* these agents must operate...
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by Mark D. Hill on Jan 12, 2026 | Tags: Accelerators, Memory, Modelling
TL;DR: Latency-tolerant architectures, e.g., GPUs, increasingly use memory/storage hierarchies, e.g., for KV Caches to speed Large-Language Model AI inference. To aid codesign of such workloads and architectures, we develop the simple PipeOrgan analytic model for...
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