Zobrazeno 1 - 10
of 38
pro vyhledávání: '"Boroumand, Amirali"'
Neural networks (NNs) are growing in importance and complexity. A neural network's performance (and energy efficiency) can be bound either by computation or memory resources. The processing-in-memory (PIM) paradigm, where computation is placed near o
Externí odkaz:
http://arxiv.org/abs/2209.08938
Today's computing systems require moving data back-and-forth between computing resources (e.g., CPUs, GPUs, accelerators) and off-chip main memory so that computation can take place on the data. Unfortunately, this data movement is a major bottleneck
Externí odkaz:
http://arxiv.org/abs/2205.14664
A growth in data volume, combined with increasing demand for real-time analysis (using the most recent data), has resulted in the emergence of database systems that concurrently support transactions and data analytics. These hybrid transactional and
Externí odkaz:
http://arxiv.org/abs/2204.11275
Autor:
Oliveira, Geraldo F., Ghose, Saugata, Gómez-Luna, Juan, Boroumand, Amirali, Savery, Alexis, Rao, Sonny, Qazi, Salman, Grignou, Gwendal, Thakur, Rahul, Shiu, Eric, Mutlu, Onur
The number and diversity of consumer devices are growing rapidly, alongside their target applications' memory consumption. Unfortunately, DRAM scalability is becoming a limiting factor to the available memory capacity in consumer devices. As a potent
Externí odkaz:
http://arxiv.org/abs/2111.02325
Autor:
Boroumand, Amirali, Ghose, Saugata, Akin, Berkin, Narayanaswami, Ravi, Oliveira, Geraldo F., Ma, Xiaoyu, Shiu, Eric, Mutlu, Onur
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 designed to fit within the limited area and energy constraints of the ed
Externí odkaz:
http://arxiv.org/abs/2109.14320
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 that concurrently support transactions and data analytics. These hybrid
Externí odkaz:
http://arxiv.org/abs/2103.00798
Autor:
Boroumand, Amirali, Ghose, Saugata, Akin, Berkin, Narayanaswami, Ravi, Oliveira, Geraldo F., Ma, Xiaoyu, Shiu, Eric, Mutlu, Onur
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 while still fitting within tight resource constraints. We analyze a com
Externí odkaz:
http://arxiv.org/abs/2103.00768
Autor:
Cali, Damla Senol, Kalsi, Gurpreet S., Bingöl, Zülal, Firtina, Can, Subramanian, Lavanya, Kim, Jeremie S., Ausavarungnirun, Rachata, Alser, Mohammed, Gomez-Luna, Juan, Boroumand, Amirali, Nori, Anant, Scibisz, Allison, Subramoney, Sreenivas, Alkan, Can, Ghose, Saugata, Mutlu, Onur
Genome sequence analysis has enabled significant advancements in medical and scientific areas such as personalized medicine, outbreak tracing, and the understanding of evolution. Unfortunately, it is currently bottlenecked by the computational power
Externí odkaz:
http://arxiv.org/abs/2009.07692
Many modern and emerging applications must process increasingly large volumes of data. Unfortunately, prevalent computing paradigms are not designed to efficiently handle such large-scale data: the energy and performance costs to move this data betwe
Externí odkaz:
http://arxiv.org/abs/1907.12947
Poor DRAM technology scaling over the course of many years has caused DRAM-based main memory to increasingly become a larger system bottleneck. A major reason for the bottleneck is that data stored within DRAM must be moved across a pin-limited memor
Externí odkaz:
http://arxiv.org/abs/1802.00320