Flash-Cosmos: In-Flash Bulk Bitwise Operations Using Inherent Computation Capability of NAND Flash Memory
Not on the map. Why?
Abstract (arXiv)
Bulk bitwise operations, i.e., bitwise operations on large bit vectors, are prevalent in a wide range of important application domains, including databases, graph processing, genome analysis, cryptography, and hyper-dimensional computing. In conventional systems, the performance and energy efficiency of bulk bitwise operations are bottlenecked by data movement between the compute units and the memory hierarchy. In-flash processing (i.e., processing data inside NAND flash chips) has a high potential to accelerate bulk bitwise operations by fundamentally reducing data movement through the entire memory hierarchy. We identify two key limitations of the state-of-the-art in-flash processing technique for bulk bitwise operations; (i) it falls short of maximally exploiting the bit-level parallelism of bulk bitwise operations; (ii) it is unreliable because it does not consider the highly error-prone nature of NAND flash memory. We propose Flash-Cosmos (Flash Computation with One-Shot Multi-Operand Sensing), a new in-flash processing technique that significantly increases the performance and energy efficiency of bulk bitwise operations while providing high reliability. Flash-Cosmos introduces two key mechanisms that can be easily supported in modern NAND flash chips: (i) Multi-Wordline Sensing (MWS), which enables bulk bitwise operations on a large number of operands with a single sensing operation, and (ii) Enhanced SLC-mode Programming (ESP), which enables reliable computation inside NAND flash memory. We demonstrate the feasibility of performing bulk bitwise operations with high reliability in Flash-Cosmos by testing 160 real 3D NAND flash chips. Our evaluation shows that Flash-Cosmos improves average performance and energy efficiency by 3.5x/32x and 3.3x/95x, respectively, over the state-of-the-art in-flash/outside-storage processing techniques across three real-world applications.
Key facts. Cited by 3 papers in the map (data through 14 Oct 2026). First posted on arXiv 12 Sep 2022.
Builds on
No reference list yet.
Built on by
Citing papers per year: 2022: 1; 2023: 0; 2024: 0; 2025: 1; 2026: 1.
3 papers.
- 2022Accelerating Neural Network Inference with Processing-in-DRAM: From the Edge to the Cloudcited by 5
- 2025REIS: A High-Performance and Energy-Efficient Retrieval System with In-Storage Processingcited by 4
- 2026Stable Spike: Dual Consistency Optimization via Bitwise AND Operations for Spiking Neural Networkscited by 0
- 2026Stable Spike: Dual Consistency Optimization via Bitwise AND Operations for Spiking Neural Networks12 Mar 2026
- 2025REIS: A High-Performance and Energy-Efficient Retrieval System with In-Storage Processing19 Jun 2025
- 2022Accelerating Neural Network Inference with Processing-in-DRAM: From the Edge to the Cloud19 Sep 2022