31 research outputs found
Enhancing Reliability of Neural Networks at the Edge: Inverted Normalization with Stochastic Affine Transformations
Bayesian Neural Networks (BayNNs) naturally provide uncertainty in their
predictions, making them a suitable choice in safety-critical applications.
Additionally, their realization using memristor-based in-memory computing (IMC)
architectures enables them for resource-constrained edge applications. In
addition to predictive uncertainty, however, the ability to be inherently
robust to noise in computation is also essential to ensure functional safety.
In particular, memristor-based IMCs are susceptible to various sources of
non-idealities such as manufacturing and runtime variations, drift, and
failure, which can significantly reduce inference accuracy. In this paper, we
propose a method to inherently enhance the robustness and inference accuracy of
BayNNs deployed in IMC architectures. To achieve this, we introduce a novel
normalization layer combined with stochastic affine transformations. Empirical
results in various benchmark datasets show a graceful degradation in inference
accuracy, with an improvement of up to
Topological and Dynamical Complexity of Random Neural Networks
Random neural networks are dynamical descriptions of randomly interconnected
neural units. These show a phase transition to chaos as a disorder parameter is
increased. The microscopic mechanisms underlying this phase transition are
unknown, and similarly to spin-glasses, shall be fundamentally related to the
behavior of the system. In this Letter we investigate the explosion of
complexity arising near that phase transition. We show that the mean number of
equilibria undergoes a sharp transition from one equilibrium to a very large
number scaling exponentially with the dimension on the system. Near
criticality, we compute the exponential rate of divergence, called topological
complexity. Strikingly, we show that it behaves exactly as the maximal Lyapunov
exponent, a classical measure of dynamical complexity. This relationship
unravels a microscopic mechanism leading to chaos which we further demonstrate
on a simpler class of disordered systems, suggesting a deep and underexplored
link between topological and dynamical complexity
Latent Replay for Real-Time Continual Learning
Training deep neural networks at the edge on light computational devices,
embedded systems and robotic platforms is nowadays very challenging. Continual
learning techniques, where complex models are incrementally trained on small
batches of new data, can make the learning problem tractable even for CPU-only
embedded devices enabling remarkable levels of adaptiveness and autonomy.
However, a number of practical problems need to be solved: catastrophic
forgetting before anything else. In this paper we introduce an original
technique named "Latent Replay" where, instead of storing a portion of past
data in the input space, we store activations volumes at some intermediate
layer. This can significantly reduce the computation and storage required by
native rehearsal. To keep the representation stable and the stored activations
valid we propose to slow-down learning at all the layers below the latent
replay one, leaving the layers above free to learn at full pace. In our
experiments we show that Latent Replay, combined with existing continual
learning techniques, achieves state-of-the-art performance on complex video
benchmarks such as CORe50 NICv2 (with nearly 400 small and highly non-i.i.d.
batches) and OpenLORIS. Finally, we demonstrate the feasibility of nearly
real-time continual learning on the edge through the deployment of the proposed
technique on a smartphone device.Comment: Pre-print v3: 13 pages, 9 figures, 10 tables, 1 algorith
JALAD: Joint Accuracy- and Latency-Aware Deep Structure Decoupling for Edge-Cloud Execution
Recent years have witnessed a rapid growth of deep-network based services and
applications. A practical and critical problem thus has emerged: how to
effectively deploy the deep neural network models such that they can be
executed efficiently. Conventional cloud-based approaches usually run the deep
models in data center servers, causing large latency because a significant
amount of data has to be transferred from the edge of network to the data
center. In this paper, we propose JALAD, a joint accuracy- and latency-aware
execution framework, which decouples a deep neural network so that a part of it
will run at edge devices and the other part inside the conventional cloud,
while only a minimum amount of data has to be transferred between them. Though
the idea seems straightforward, we are facing challenges including i) how to
find the best partition of a deep structure; ii) how to deploy the component at
an edge device that only has limited computation power; and iii) how to
minimize the overall execution latency. Our answers to these questions are a
set of strategies in JALAD, including 1) A normalization based in-layer data
compression strategy by jointly considering compression rate and model
accuracy; 2) A latency-aware deep decoupling strategy to minimize the overall
execution latency; and 3) An edge-cloud structure adaptation strategy that
dynamically changes the decoupling for different network conditions.
Experiments demonstrate that our solution can significantly reduce the
execution latency: it speeds up the overall inference execution with a
guaranteed model accuracy loss.Comment: conference, copyright transfered to IEE
MaestROB: A Robotics Framework for Integrated Orchestration of Low-Level Control and High-Level Reasoning
This paper describes a framework called MaestROB. It is designed to make the
robots perform complex tasks with high precision by simple high-level
instructions given by natural language or demonstration. To realize this, it
handles a hierarchical structure by using the knowledge stored in the forms of
ontology and rules for bridging among different levels of instructions.
Accordingly, the framework has multiple layers of processing components;
perception and actuation control at the low level, symbolic planner and Watson
APIs for cognitive capabilities and semantic understanding, and orchestration
of these components by a new open source robot middleware called Project Intu
at its core. We show how this framework can be used in a complex scenario where
multiple actors (human, a communication robot, and an industrial robot)
collaborate to perform a common industrial task. Human teaches an assembly task
to Pepper (a humanoid robot from SoftBank Robotics) using natural language
conversation and demonstration. Our framework helps Pepper perceive the human
demonstration and generate a sequence of actions for UR5 (collaborative robot
arm from Universal Robots), which ultimately performs the assembly (e.g.
insertion) task.Comment: IEEE International Conference on Robotics and Automation (ICRA) 2018.
Video: https://www.youtube.com/watch?v=19JsdZi0TW
NeuSpin: Design of a Reliable Edge Neuromorphic System Based on Spintronics for Green AI
Internet of Things (IoT) and smart wearable devices for personalized
healthcare will require storing and computing ever-increasing amounts of data.
The key requirements for these devices are ultra-low-power, high-processing
capabilities, autonomy at low cost, as well as reliability and accuracy to
enable Green AI at the edge. Artificial Intelligence (AI) models, especially
Bayesian Neural Networks (BayNNs) are resource-intensive and face challenges
with traditional computing architectures due to the memory wall problem.
Computing-in-Memory (CIM) with emerging resistive memories offers a solution by
combining memory blocks and computing units for higher efficiency and lower
power consumption. However, implementing BayNNs on CIM hardware, particularly
with spintronic technologies, presents technical challenges due to variability
and manufacturing defects. The NeuSPIN project aims to address these challenges
through full-stack hardware and software co-design, developing novel
algorithmic and circuit design approaches to enhance the performance,
energy-efficiency and robustness of BayNNs on sprintronic-based CIM platforms