Pinatubo: A processing-in-memory architecture for bulk bitwise operations in emerging non-volatile memories
By Shuangchen Li, Cong Xu, Qiaosha Zou, Jishen Zhao, Yu Lu, Yuan Xie. Design Automation Conference, 2016
445 citations recorded by the source corpus.
Subfields: Electrical and Electronic Engineering, Hardware and Architecture
View Paper: https://www.semanticscholar.org/paper/571acc0feaffe3342845ff80e554ea7d1ad741c5
How researchers see this paper
The researchers whose trusted networks rank it highest, strongest advocate first. Every researcher gets a different order.
In Electrical and Electronic Engineering
- Abu Sebastian
- M. Le Gallo
- Onur Mutlu
- Saugata Ghose
- Rachata Ausavarungnirun
- Evangelos Eleftheriou
- Juan Gómez-Luna
- Yuan Xie
In Hardware and Architecture
- Saugata Ghose
- Rachata Ausavarungnirun
- Onur Mutlu
- Juan Gómez-Luna
- Yuan Xie
- Abu Sebastian
- M. Le Gallo
- Dimin Niu
Citations in the corpus
- Compute Caches (2017, background): “Recent research enhanced non-volatile memory technology to support certain in-memory CAM [16] and bitwise logic operations [30].”
- RowHammer: A Retrospective (2019, unknown): “, [13]–[15], [18], [22], [24], [34]–[36], [56], [63], [72], [77], [78], [80], [91], [92], [95], [96], [99], [101], [102], [111], [121]–[123], [151], [153], [154], [160], [161], [172], [178], [179], [182], [195], [198], [215]–[219], [221], [222], [228], [257], and [260]).”
- PRIME: A Novel Processing-in-Memory Architecture for Neural Network Computation in ReRAM-Based Main Memory (2016, unknown): “Also, while previous work focused on database and graph processing applications [3], [5], [59], PRIME aims at accelerating NN applications.”
- Memory devices and applications for in-memory computing (2020, background): “NaTuRe NaNoTecHNology This non-volatile accumulative behaviour, in spite of its nonlinear and stochastic nature, can be exploited in several applications, such as training deep neural networks, where the conductance values need to be incrementally modified.”
- Processing Data Where It Makes Sense: Enabling In-Memory Computation (2019, methodology): “A number of Ambit-like bitwise operation substrates have been proposed in recent years, making use of emerging resistive memory technologies, e.g., phasechange memory (PCM) [17, 19, 176, 177, 178, 179], SRAM, or specialized computational DRAM.”
- DRISA: A DRAM-based Reconfigurable In-Situ Accelerator (2017, background): “On the other hand, designing a latch for each local WL and serially decoding for the active rows [60] wastes too much latency and energy.”