4 papers
CACHEFORGE: LLM-Guided End-to-End Generative Cache Replacement Policy for Performance and Hardware Efficiency
Kaushal Mhapsekar, Bita Aslrousta, Brijesh Kumar Bhayana +5
Modern cache replacement designs saturate because they operate within fixed representational structures, hand-crafted and heuristic based feature-engineered predictors, or offline…
CacheFit: The Rising Cost of Cache Replacement and Constraint-Driven Redesign of Last-Level Cache Replacement Policies
Bita Aslrousta, Kaushal Mhapsekar, Darsh Asher +3
The latency, power, and area costs of LLC replacement remain poorly explored not because simulators cannot be instrumented--event counters feeding a power model are routine--but be…
TranScope: What the Software Hides About LLM Training Data, the Hardware Reveals at Scale, and Accelerators Magnify
Joshua Kalyanapu, Darsh Asher, Kaushal Mhapsekar +5
Membership is the root privacy primitive in machine learning: to date, no hardware-based out-of-distribution detection on black-box models has been demonstrated against constant-ti…
CacheMind: From Miss Rates to Why -- Natural-Language, Trace-Grounded Reasoning for Cache Replacement
Kaushal Mhapsekar, Azam Ghanbari, Bita Aslrousta +1
Cache replacement remains a challenging problem in CPU microarchitecture, often addressed using hand-crafted heuristics, limiting cache performance. Cache data analysis requires pa…