269 citations · 1.1k across the 57 of their papers we have counts for
8 papers · 1 filter
Extending Memory Capacity in Consumer Devices with Emerging Non-Volatile Memory: An Experimental Study
Geraldo F. Oliveira, Saugata Ghose, Juan Gómez-Luna +8
The number and diversity of consumer devices are growing rapidly, alongside their target applications' memory consumption. Unfortunately, DRAM scalability is becoming a limiting fa…
Google Neural Network Models for Edge Devices: Analyzing and Mitigating Machine Learning Inference Bottlenecks
Amirali Boroumand, Saugata Ghose, Berkin Akin +5
Emerging edge computing platforms often contain machine learning (ML) accelerators that can accelerate inference for a wide range of neural network (NN) models. These models are de…
SIMDRAM: An End-to-End Framework for Bit-Serial SIMD Computing in DRAM
Nastaran Hajinazar, Geraldo F. Oliveira, Sven Gregorio +7
Processing-using-DRAM has been proposed for a limited set of basic operations (i.e., logic operations, addition). However, in order to enable full adoption of processing-using-DRAM…
CODIC: A Low-Cost Substrate for Enabling Custom In-DRAM Functionalities and Optimizations
Lois Orosa, Yaohua Wang, Mohammad Sadrosadati +10
DRAM is the dominant main memory technology used in modern computing systems. Computing systems implement a memory controller that interfaces with DRAM via DRAM commands. DRAM exec…
DAMOV: A New Methodology and Benchmark Suite for Evaluating Data Movement Bottlenecks
Geraldo F. Oliveira, Juan Gómez-Luna, Lois Orosa +5
Data movement between the CPU and main memory is a first-order obstacle against improving performance, scalability, and energy efficiency in modern systems. Computer systems employ…
Polynesia: Enabling Effective Hybrid Transactional/Analytical Databases with Specialized Hardware/Software Co-Design
Amirali Boroumand, Saugata Ghose, Geraldo F. Oliveira +1
An exponential growth in data volume, combined with increasing demand for real-time analysis (i.e., using the most recent data), has resulted in the emergence of database systems t…
Mitigating Edge Machine Learning Inference Bottlenecks: An Empirical Study on Accelerating Google Edge Models
Amirali Boroumand, Saugata Ghose, Berkin Akin +5
As the need for edge computing grows, many modern consumer devices now contain edge machine learning (ML) accelerators that can compute a wide range of neural network (NN) models w…
BlockHammer: Preventing RowHammer at Low Cost by Blacklisting Rapidly-Accessed DRAM Rows
Abdullah Giray Yağlıkçı, Minesh Patel, Jeremie S. Kim +9
Aggressive memory density scaling causes modern DRAM devices to suffer from RowHammer, a phenomenon where rapidly activating a DRAM row can cause bit-flips in physically-nearby row…