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PuDGhost: Experimental Analysis of Computation Result Corruption in Processing-using-DRAM Operations on Real DRAM Chips and Implications for Future Systems

Published 17 Jun 2026 in cs.AR and cs.DC | (2606.19119v1)

Abstract: Processing-using-DRAM (PuD) is a promising computation paradigm that alleviates frequent data movement between main memory and processing units by using each DRAM column as a computation engine via simultaneous multiple-row activation (SiMRA). Unfortunately, DRAM density scaling may hinder PuD's benefits: denser cell arrays bring rows and columns closer, making regular DRAM operations susceptible to noise and interference from neighboring cells. Yet no prior work investigates whether interference from rows or columns not intended to participate in computation can compromise PuD robustness. In this work, we reveal PuDGhost, an interference phenomenon where a PuD operation in a given column produces erroneous results due to interference from 1) data in non-activated DRAM rows and 2) data in other columns that compute concurrently under the same SiMRA operation. PuDGhost violates the ideal picture that each column's computation depends solely on its own operand data, threatening future PuD systems. We present the first extensive characterization of PuDGhost using 96 real DDR4 DRAM chips from 12 modules, quantifying these two interference sources under various conditions. Among our 15 new empirical observations, we highlight two major results: 1) data in adjacent non-activated rows affects SiMRA outputs by up to 10% for random inputs, and 2) data in concurrently computing columns affects SiMRA outputs by up to 48% for random inputs. Guided by these findings, we propose countermeasures across multiple layers of the PuD computing stack. Specifically, we evaluate on real DDR4 DRAM chips: 1) robust column screening that reduces the risk of using unreliable columns in the presence of PuDGhost, and 2) a compute row layout that mitigates PuDGhost via dedicated rows between compute rows. Our solutions greatly improve PuD computation accuracy and provide a foundation for robust future PuD systems.

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