10 citations · 13 across the 2 of their papers we have counts for
12 papers
Automated Backend-Aware Post-Training Quantization
Ziheng Jiang, Animesh Jain, Andrew Liu +4
Quantization is a key technique to reduce the resource requirement and improve the performance of neural network deployment. However, different hardware backends such as x86 CPU, N…
Cortex: A Compiler for Recursive Deep Learning Models
Pratik Fegade, Tianqi Chen, Phillip B. Gibbons +1
Optimizing deep learning models is generally performed in two steps: (i) high-level graph optimizations such as kernel fusion and (ii) low level kernel optimizations such as those…
Dynamic Tensor Rematerialization
Marisa Kirisame, Steven Lyubomirsky, Altan Haan +5
Checkpointing enables the training of deep learning models under restricted memory budgets by freeing intermediate activations from memory and recomputing them on demand. Current c…
Relay: A High-Level Compiler for Deep Learning
Jared Roesch, Steven Lyubomirsky, Marisa Kirisame +7
Frameworks for writing, compiling, and optimizing deep learning (DL) models have recently enabled progress in areas like computer vision and natural language processing. Extending…
ADARES: Adaptive Resource Management for Virtual Machines
Ignacio Cano, Lequn Chen, Pedro Fonseca +5
Virtual execution environments allow for consolidation of multiple applications onto the same physical server, thereby enabling more efficient use of server resources. However, use…
Automating Generation of Low Precision Deep Learning Operators
Meghan Cowan, Thierry Moreau, Tianqi Chen +1
State of the art deep learning models have made steady progress in the fields of computer vision and natural language processing, at the expense of growing model sizes and computat…