Memory footprint

Memory footprint refers to the amount of main memory that a program uses or references while running. The word footprint generally refers to the extent of physical dimensions that an object occupies, giving a sense of its size. In computing, the memory footprint of a software application indicates its runtime memory requirements, while the program executes. This includes all sorts of active memory regions like code segment containing (mostly) program instructions (and occasionally constants), data segment (both initialized and uninitialized), heap memory, call stack, plus memory required to hold any additional data structures, such as symbol tables, debugging data structures, open files, shared libraries mapped to the current process, etc., that the program ever needs while executing and will be loaded at least once during the entire run. Larger programs have larger memory footprints. An application's memory footprint is roughly proportion

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Unix

Unix, officiellement UNIX, est une famille de systèmes d'exploitation multitâche et multi-utilisateur dérivé du Unix d'origine créé par AT&T, le développement de ce dernier ayant commencé dans les ann

CS-503: Visual intelligence : machines and minds

The course will discuss classic material as well as recent advances in computer vision and machine learning relevant to processing visual data with a primary focus on embodied intelligence and vision for active agents.

DH-405: Foundations of digital humanities

This course gives an introduction to the fundamental concepts and methods of the Digital Humanities, both from a theoretical and applied point of view. The course introduces the Digital Humanities circle of processing and interpretation, from data acquisition to new understandings.

Better Loop Fusion for LMS

Véra Salvisberg

This is my master thesis done at PPL in Stanford under the supervision of Prof. Kunle Olukotun. It improved LMS, a framework for embedding DSLs (domain-specific languages) into Scala which features many general optimizations that can be used by any DSLs for free. I implemented a more powerful and cleaner version of the loop fusion optimization from the compiler world. Loop fusion is an important performance optimization for all languages that feature list comprehensions and translate their high-level operations into loop-based representations. It can decrease runtime, memory footprint and code size through two different fusion cases: The simpler one is called horizontal or side-by-side fusion and fuses adjacent loops iterating over the same range, enabling further optimizations. The second one is vertical or pipeline fusion, where a producer and a consumer of data are fused, removing the need for the intermediate data structure.

2014

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Hardware-oriented pruning and quantization of Deep Learning models to detect life-threatening arrhythmias

Lizeth Gonzalez Carabarin, Alexandre Schmid

Wearable solutions based on Deep Learning (DL) for real-time ECG monitoring are a promising alternative to detect life-threatening arrhythmias. However, DL models suffer of a large memory footprint, which hampers their adoption in portable technologies. Therefore, we leverage a hardware-oriented pruning approach to effectively shrink DL models. We demonstrate that tiny DL models can be reduced to 5.55x (pruning), and 26.6x (pruning+quantization) compression rate, with 82.9% FLOP's reduction. These ultra-compressed models are able to effectively classify life-threatening arrhythmias with minimal or no loss of performance compared with their non=pruned counterparts, which can pave the path towards DL-based biomedical portable solutions.

IEEE2021

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Direction is what you need: Improving Word Embedding Compression in Large Language Models

Karl Aberer, Mohammadreza Banaei, Rémi Philippe Lebret

The adoption of Transformer-based models in natural language processing (NLP) has led to great success using a massive number of parameters. However, due to deployment constraints in edge devices, there has been a rising interest in the compression of these models to improve their inference time and memory footprint. This paper presents a novel loss objective to compress token embeddings in the Transformer-based models by leveraging an AutoEncoder architecture. More specifically, we emphasize the importance of the direction of compressed embeddings with respect to original uncompressed embeddings. The proposed method is task-agnostic and does not require further language modeling pre-training. Our method significantly outperforms the commonly used SVD-based matrix-factorization approach in terms of initial language model Perplexity. Moreover, we evaluate our proposed approach over SQuAD v1.1 dataset and several downstream tasks from the GLUE benchmark, where we also outperform the baseline in most scenarios. Our code is public.(1).

ASSOC COMPUTATIONAL LINGUISTICS-ACL2021

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