iree-org/iree
Papers Referenced in This Repository
Quantization and Training of Neural Networks for Efficient Integer-Arithmetic-Only Inference
The rising popularity of intelligent mobile devices and the daunting computational cost of deep learning-based models call for efficient and accurate on-device inference schemes. We propose a quantization scheme that allows inference to be carried out using integer-only arithmetic, which can be impl...
FP8 Formats for Deep Learning
FP8 is a natural progression for accelerating deep learning training inference beyond the 16-bit formats common in modern processors. In this paper we propose an 8-bit floating point (FP8) binary interchange format consisting of two encodings - E4M3 (4-bit exponent and 3-bit mantissa) and E5M2 (5-bi...
8-bit Numerical Formats for Deep Neural Networks
Given the current trend of increasing size and complexity of machine learning architectures, it has become of critical importance to identify new approaches to improve the computational efficiency of model training. In this context, we address the advantages of floating-point over fixed-point repres...
FlashAttention-2: Faster Attention with Better Parallelism and Work Partitioning
Scaling Transformers to longer sequence lengths has been a major problem in the last several years, promising to improve performance in language modeling and high-resolution image understanding, as well as to unlock new applications in code, audio, and video generation. The attention layer is the ma...
Optimizing Winograd-Based Convolution with Tensor Cores.
Convolution computing is one of the primary time consuming part of convolutional neural networks (CNNs). State of the art convolutional neural networks use samll, 3 × 3 filters. Recent work on Winograd convolution can reduce the computational complexity a lot, making the convolution computing fast. ...
Fast splittable pseudorandom number generators
We describe a new algorithm SplitMix for an object-oriented and splittable pseudorandom number generator (PRNG) that is quite fast: 9 64-bit arithmetic/logical operations per 64 bits generated. A conventional linear PRNG object provides a generate method that returns one pseudorandom value and updat...
Nimble: Efficiently Compiling Dynamic Neural Networks for Model Inference
Modern deep neural networks increasingly make use of features such as dynamic control flow, data structures and dynamic tensor shapes. Existing deep learning systems focus on optimizing and executing static neural networks which assume a pre-determined model architecture and input data shapes--assum...