AgentDSE uses an LLM agent in a simulator-in-the-loop setup to achieve competitive or superior architectural designs with up to 100x fewer evaluations than traditional black-box optimization methods.
The championship simulator: Architectural simulation for education and competition
5 Pith papers cite this work. Polarity classification is still indexing.
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2026 5representative citing papers
IP-CaT jointly optimizes TLB and cache management for L1I prefetching via a translation prefetch buffer and trimodal replacement policy, yielding 8.7% geomean speedup over EPI across 105 server workloads.
CHIA is a distributed graph-based framework for composing, deploying, and verifying agentic AI hardware/software co-design flows, demonstrated on RTL, simulation, and compiler tasks.
An LLM-driven agentic system evolves microarchitectural policies for cache replacement, data prefetching, and branch prediction, producing designs that match or exceed prior state-of-the-art in IPC on standard benchmarks.
Dynamic selection between two cache/prefetch policies reduces mean IPC loss from 1.54% to 0.11% versus an oracle across 490 workload phases.
citing papers explorer
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AgentDSE: Reasoning-Augmented Architectural Design Space Exploration
AgentDSE uses an LLM agent in a simulator-in-the-loop setup to achieve competitive or superior architectural designs with up to 100x fewer evaluations than traditional black-box optimization methods.
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Enhancing Instruction Prefetching via Cache and TLB Management
IP-CaT jointly optimizes TLB and cache management for L1I prefetching via a translation prefetch buffer and trimodal replacement policy, yielding 8.7% geomean speedup over EPI across 105 server workloads.
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CHIA: An open-source framework for principled, agentic AI-driven hardware/software co-design research
CHIA is a distributed graph-based framework for composing, deploying, and verifying agentic AI hardware/software co-design flows, demonstrated on RTL, simulation, and compiler tasks.
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Agentic Architect: An Agentic AI Framework for Architecture Design Exploration and Optimization
An LLM-driven agentic system evolves microarchitectural policies for cache replacement, data prefetching, and branch prediction, producing designs that match or exceed prior state-of-the-art in IPC on standard benchmarks.
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Beyond Static Policies: Exploring Dynamic Policy Selection for Single-Thread Performance Optimization
Dynamic selection between two cache/prefetch policies reduces mean IPC loss from 1.54% to 0.11% versus an oracle across 490 workload phases.