4 papers
L3: Accelerator-Friendly Lossless Image Format for High-Resolution, High-Throughput DNN Training
Jonghyun Bae, Woohyeon Baek, Tae Jun Ham +1
The training process of deep neural networks (DNNs) is usually pipelined with stages for data preparation on CPUs followed by gradient computation on accelerators like GPUs. In an…
Mithril: Cooperative Row Hammer Protection on Commodity DRAM Leveraging Managed Refresh
Michael Jaemin Kim, Jaehyun Park, Yeonhong Park +5
Since its public introduction in the mid-2010s, the Row Hammer (RH) phenomenon has drawn significant attention from the research community due to its security implications. Althoug…
The MosaicSim Simulator (Full Technical Report)
Opeoluwa Matthews, Aninda Manocha, Davide Giri +7
As Moore's Law has slowed and Dennard Scaling has ended, architects are increasingly turning to heterogeneous parallelism and domain-specific hardware-software co-designs. These tr…
A: Accelerating Attention Mechanisms in Neural Networks with Approximation
Tae Jun Ham, Sung Jun Jung, Seonghak Kim +8
With the increasing computational demands of neural networks, many hardware accelerators for the neural networks have been proposed. Such existing neural network accelerators often…