Publications

Memory-Aware Functional IR for Higher-Level Synthesis of Accelerators
Christof Schlaak
Tzung-Han Juang
Christophe Dubach
2022-01-31
ACM Transactions on Architecture and Code Optimization (publié)
doi.org
Memory-Aware Functional IR for Higher-Level Synthesis of Accelerators
Christof Schlaak
Tzung-Han Juang
Christophe Dubach
Specialized accelerators deliver orders of a magnitude of higher performance than general-purpose processors. The ever-changing nature of mo… (voir plus)dern workloads is pushing the adoption of Field Programmable Gate Arrays (FPGAs) as the substrate of choice. However, FPGAs are hard to program directly using Hardware Description Languages (HDLs). Even modern high-level HDLs, e.g., Spatial and Chisel, still require hardware expertise. This article adopts functional programming concepts to provide a hardware-agnostic higher-level programming abstraction. During synthesis, these abstractions are mechanically lowered into a functional Intermediate Representation (IR) that defines a specific hardware design point. This novel IR expresses different forms of parallelism and standard memory features such as asynchronous off-chip memories or synchronous on-chip buffers. Exposing such features at the IR level is essential for achieving high performance. The viability of this approach is demonstrated on two stencil computations and by exploring the optimization space of matrix-matrix multiplication. Starting from a high-level representation for these algorithms, our compiler produces low-level VHSIC Hardware Description Language (VHDL) code automatically. Several design points are evaluated on an Intel Arria 10 FPGA, demonstrating the ability of the IR to exploit different hardware features. This article also shows that the designs produced are competitive with highly tuned OpenCL implementations and outperform hardware-agnostic OpenCL code.
2022-01-31
ACM Transactions on Architecture and Code Optimization (TACO) (published)
doi.org
Fortuitous Forgetting in Connectionist Networks
Hattie Zhou
Ankit Vani
Hugo Larochelle
Aaron Courville
Forgetting is often seen as an unwanted characteristic in both human and machine learning. However, we propose that forgetting can in fact b… (voir plus)e favorable to learning. We introduce"forget-and-relearn"as a powerful paradigm for shaping the learning trajectories of artificial neural networks. In this process, the forgetting step selectively removes undesirable information from the model, and the relearning step reinforces features that are consistently useful under different conditions. The forget-and-relearn framework unifies many existing iterative training algorithms in the image classification and language emergence literature, and allows us to understand the success of these algorithms in terms of the disproportionate forgetting of undesirable information. We leverage this understanding to improve upon existing algorithms by designing more targeted forgetting operations. Insights from our analysis provide a coherent view on the dynamics of iterative training in neural networks and offer a clear path towards performance improvements.
2022-01-28
ICLR.cc/2022/Conference (poster)
openreview.net
openreview.net
Medical Doctors in Health Reforms
Jean-Louis Denis
Sabrina Germain
Catherine Régis
Gianluca Veronesi
Health and legal experts from England and Canada consider the influence of medical doctors on reforms in this comparative study. With reflec… (voir plus)tions on participation since the inception of publicly funded healthcare systems, they show how the status of doctors affects change.
2022-01-28
(publié)
doi.org
New Insights on Reducing Abrupt Representation Change in Online Continual Learning
Lucas Caccia
Rahaf Aljundi
Nader Asadi
Tinne Tuytelaars
Joelle Pineau
Eugene Belilovsky
In the online continual learning paradigm, agents must learn from a changing distribution while respecting memory and compute constraints. E… (voir plus)xperience Replay (ER), where a small subset of past data is stored and replayed alongside new data, has emerged as a simple and effective learning strategy. In this work, we focus on the change in representations of observed data that arises when previously unobserved classes appear in the incoming data stream, and new classes must be distinguished from previous ones. We shed new light on this question by showing that applying ER causes the newly added classes’ representations to overlap significantly with the previous classes, leading to highly disruptive parameter updates. Based on this empirical analysis, we propose a new method which mitigates this issue by shielding the learned representations from drastic adaptation to accommodate new classes. We show that using an asymmetric update rule pushes new classes to adapt to the older ones (rather than the reverse), which is more effective especially at task boundaries, where much of the forgetting typically occurs. Empirical results show significant gains over strong baselines on standard continual learning benchmarks.
2022-01-28
ICLR.cc/2022/Conference (poster)
doi.org
openreview.net
R5: Rule Discovery with Reinforced and Recurrent Relational Reasoning
Shengyao Lu
Bang Liu
Keith G Mills
SHANGLING JUI
Di Niu
Systematicity, i.e., the ability to recombine known parts and rules to form new sequences while reasoning over relational data, is critical … (voir plus)to machine intelligence. A model with strong systematicity is able to train on small-scale tasks and generalize to large-scale tasks. In this paper, we propose R5, a relational reasoning framework based on reinforcement learning that reasons over relational graph data and explicitly mines underlying compositional logical rules from observations. R5 has strong systematicity and being robust to noisy data. It consists of a policy value network equipped with Monte Carlo Tree Search to perform recurrent relational prediction and a backtrack rewriting mechanism for rule mining. By alternately applying the two components, R5 progressively learns a set of explicit rules from data and performs explainable and generalizable relation prediction. We conduct extensive evaluations on multiple datasets. Experimental results show that R5 outperforms various embedding-based and rule induction baselines on relation prediction tasks while achieving a high recall rate in discovering ground truth rules.
2022-01-28
ICLR.cc/2022/Conference (spotlight)
openreview.net
openreview.net
Lacking social support is associated with structural divergences in hippocampus–default network co-variation patterns
Chris Zajner
Nathan Spreng
Danilo Bzdok
2022-01-27
Social Cognitive and Affective Neuroscience (publié)
doi.org
Multilevel development of cognitive abilities in an artificial neural network
Konstantin Volzhenin
J. Changeux
Guillaume Dumas
Several neuronal mechanisms have been proposed to account for the formation of cognitive abilities through postnatal interactions with the p… (voir plus)hysical and socio-cultural environment. Here, we introduce a three-level computational model of information processing and acquisition of cognitive abilities. We propose minimal architectural requirements to build these levels and how the parameters affect their performance and relationships. The first sensorimotor level handles local nonconscious processing, here during a visual classification task. The second level or cognitive level globally integrates the information from multiple local processors via long-ranged connections and synthesizes it in a global, but still nonconscious manner. The third and cognitively highest level handles the information globally and consciously. It is based on the Global Neuronal Workspace (GNW) theory and is referred to as conscious level. We use trace and delay conditioning tasks to, respectively, challenge the second and third levels. Results first highlight the necessity of epigenesis through selection and stabilization of synapses at both local and global scales to allow the network to solve the first two tasks. At the global scale, dopamine appears necessary to properly provide credit assignment despite the temporal delay between perception and reward. At the third level, the presence of interneurons becomes necessary to maintain a self-sustained representation within the GNW in the absence of sensory input. Finally, while balanced spontaneous intrinsic activity facilitates epigenesis at both local and global scales, the balanced excitatory-inhibitory ratio increases performance. Finally, we discuss the plausibility of the model in both neurodevelopmental and artificial intelligence terms.
2022-01-25
bioRxiv (preprint)
doi.org
Multilevel development of cognitive abilities in an artificial neural network
Konstantin Volzhenin
Jean-Pierre Changeux
Guillaume Dumas
Several neuronal mechanisms have been proposed to account for the formation of cognitive abilities through postnatal interactions with the p… (voir plus)hysical and socio-cultural environment. Here, we introduce a three-level computational model of information processing and acquisition of cognitive abilities. We propose minimal architectural requirements to build these levels and how the parameters affect their performance and relationships. The first sensorimotor level handles local nonconscious processing, here during a visual classification task. The second level or cognitive level globally integrates the information from multiple local processors via long-ranged connections and synthesizes it in a global, but still nonconscious manner. The third and cognitively highest level handles the information globally and consciously. It is based on the Global Neuronal Workspace (GNW) theory and is referred to as conscious level. We use trace and delay conditioning tasks to, respectively, challenge the second and third levels. Results first highlight the necessity of epigenesis through selection and stabilization of synapses at both local and global scales to allow the network to solve the first two tasks. At the global scale, dopamine appears necessary to properly provide credit assignment despite the temporal delay between perception and reward. At the third level, the presence of interneurons becomes necessary to maintain a self-sustained representation within the GNW in the absence of sensory input. Finally, while balanced spontaneous intrinsic activity facilitates epigenesis at both local and global scales, the balanced excitatory-inhibitory ratio increases performance. Finally, we discuss the plausibility of the model in both neurodevelopmental and artificial intelligence terms.
2022-01-25
bioRxiv (prépublication)
doi.org
Neural correlates of local parallelism during naturalistic vision
John Wilder
Morteza Rezanejad
Sven J. Dickinson
Kaleem Siddiqi
A. Jepson
Dirk. B. Walther
Human observers can rapidly perceive complex real-world scenes. Grouping visual elements into meaningful units is an integral part of this p… (voir plus)rocess. Yet, so far, the neural underpinnings of perceptual grouping have only been studied with simple lab stimuli. We here uncover the neural mechanisms of one important perceptual grouping cue, local parallelism. Using a new, image-computable algorithm for detecting local symmetry in line drawings and photographs, we manipulated the local parallelism content of real-world scenes. We decoded scene categories from patterns of brain activity obtained via functional magnetic resonance imaging (fMRI) in 38 human observers while they viewed the manipulated scenes. Decoding was significantly more accurate for scenes containing strong local parallelism compared to weak local parallelism in the parahippocampal place area (PPA), indicating a central role of parallelism in scene perception. To investigate the origin of the parallelism signal we performed a model-based fMRI analysis of the public BOLD5000 dataset, looking for voxels whose activation time course matches that of the locally parallel content of the 4916 photographs viewed by the participants in the experiment. We found a strong relationship with average local symmetry in visual areas V1-4, PPA, and retrosplenial cortex (RSC). Notably, the parallelism-related signal peaked first in V4, suggesting V4 as the site for extracting paralleism from the visual input. We conclude that local parallelism is a perceptual grouping cue that influences neuronal activity throughout the visual hierarchy, presumably starting at V4. Parallelism plays a key role in the representation of scene categories in PPA.
2022-01-21
PLoS ONE (published)
doi.org
Neural correlates of local parallelism during naturalistic vision
John Wilder
Morteza Rezanejad
Sven Dickinson
Kaleem Siddiqi
Allan Jepson
Dirk B. Walther
Human observers can rapidly perceive complex real-world scenes. Grouping visual elements into meaningful units is an integral part of this p… (voir plus)rocess. Yet, so far, the neural underpinnings of perceptual grouping have only been studied with simple lab stimuli. We here uncover the neural mechanisms of one important perceptual grouping cue, local parallelism. Using a new, image-computable algorithm for detecting local symmetry in line drawings and photographs, we manipulated the local parallelism content of real-world scenes. We decoded scene categories from patterns of brain activity obtained via functional magnetic resonance imaging (fMRI) in 38 human observers while they viewed the manipulated scenes. Decoding was significantly more accurate for scenes containing strong local parallelism compared to weak local parallelism in the parahippocampal place area (PPA), indicating a central role of parallelism in scene perception. To investigate the origin of the parallelism signal we performed a model-based fMRI analysis of the public BOLD5000 dataset, looking for voxels whose activation time course matches that of the locally parallel content of the 4916 photographs viewed by the participants in the experiment. We found a strong relationship with average local symmetry in visual areas V1-4, PPA, and retrosplenial cortex (RSC). Notably, the parallelism-related signal peaked first in V4, suggesting V4 as the site for extracting paralleism from the visual input. We conclude that local parallelism is a perceptual grouping cue that influences neuronal activity throughout the visual hierarchy, presumably starting at V4. Parallelism plays a key role in the representation of scene categories in PPA.
2022-01-21
PLoS ONE (publié)
doi.org
Digital Ageism: Challenges and Opportunities in Artificial Intelligence for Older Adults
Charlene H Chu
Rune Nyrup
Kathleen Leslie
Jiamin Shi
Andria Bianchi
Alexandra Lyn
Molly McNicholl
Shehroz S Khan
Samira Abbasgholizadeh-Rahimi
A. Grenier
Abstract Artificial intelligence (AI) and machine learning are changing our world through their impact on sectors including health care, edu… (voir plus)cation, employment, finance, and law. AI systems are developed using data that reflect the implicit and explicit biases of society, and there are significant concerns about how the predictive models in AI systems amplify inequity, privilege, and power in society. The widespread applications of AI have led to mainstream discourse about how AI systems are perpetuating racism, sexism, and classism; yet, concerns about ageism have been largely absent in the AI bias literature. Given the globally aging population and proliferation of AI, there is a need to critically examine the presence of age-related bias in AI systems. This forum article discusses ageism in AI systems and introduces a conceptual model that outlines intersecting pathways of technology development that can produce and reinforce digital ageism in AI systems. We also describe the broader ethical and legal implications and considerations for future directions in digital ageism research to advance knowledge in the field and deepen our understanding of how ageism in AI is fostered by broader cycles of injustice.
2022-01-20
The gerontologist (published)
doi.org