81 citations · 83 across the 4 of their papers we have counts for
5 papers
EcoFlow: Efficient Convolutional Dataflows for Low-Power Neural Network Accelerators
Lois Orosa, Skanda Koppula, Yaman Umuroglu +5
Dilated and transposed convolutions are widely used in modern convolutional neural networks (CNNs). These kernels are used extensively during CNN training and inference of applicat…
SISA: Set-Centric Instruction Set Architecture for Graph Mining on Processing-in-Memory Systems
Maciej Besta, Raghavendra Kanakagiri, Grzegorz Kwasniewski +15
Simple graph algorithms such as PageRank have been the target of numerous hardware accelerators. Yet, there also exist much more complex graph mining algorithms for problems such a…
The Virtual Block Interface: A Flexible Alternative to the Conventional Virtual Memory Framework
Nastaran Hajinazar, Pratyush Patel, Minesh Patel +7
Computers continue to diversify with respect to system designs, emerging memory technologies, and application memory demands. Unfortunately, continually adapting the conventional v…
SMASH: Co-designing Software Compression and Hardware-Accelerated Indexing for Efficient Sparse Matrix Operations
Konstantinos Kanellopoulos, Nandita Vijaykumar, Christina Giannoula +6
Important workloads, such as machine learning and graph analytics applications, heavily involve sparse linear algebra operations. These operations use sparse matrix compression as…
EDEN: Enabling Energy-Efficient, High-Performance Deep Neural Network Inference Using Approximate DRAM
Skanda Koppula, Lois Orosa, Abdullah Giray Yağlıkçı +4
The effectiveness of deep neural networks (DNN) in vision, speech, and language processing has prompted a tremendous demand for energy-efficient high-performance DNN inference syst…