8,274 research outputs found
Integrated information increases with fitness in the evolution of animats
One of the hallmarks of biological organisms is their ability to integrate
disparate information sources to optimize their behavior in complex
environments. How this capability can be quantified and related to the
functional complexity of an organism remains a challenging problem, in
particular since organismal functional complexity is not well-defined. We
present here several candidate measures that quantify information and
integration, and study their dependence on fitness as an artificial agent
("animat") evolves over thousands of generations to solve a navigation task in
a simple, simulated environment. We compare the ability of these measures to
predict high fitness with more conventional information-theoretic processing
measures. As the animat adapts by increasing its "fit" to the world,
information integration and processing increase commensurately along the
evolutionary line of descent. We suggest that the correlation of fitness with
information integration and with processing measures implies that high fitness
requires both information processing as well as integration, but that
information integration may be a better measure when the task requires memory.
A correlation of measures of information integration (but also information
processing) and fitness strongly suggests that these measures reflect the
functional complexity of the animat, and that such measures can be used to
quantify functional complexity even in the absence of fitness data.Comment: 27 pages, 8 figures, one supplementary figure. Three supplementary
video files available on request. Version commensurate with published text in
PLoS Comput. Bio
Predictive intelligence to the edge through approximate collaborative context reasoning
We focus on Internet of Things (IoT) environments where a network of sensing and computing devices are responsible to locally process contextual data, reason and collaboratively infer the appearance of a specific phenomenon (event). Pushing processing and knowledge inference to the edge of the IoT network allows the complexity of the event reasoning process to be distributed into many manageable pieces and to be physically located at the source of the contextual information. This enables a huge amount of rich data streams to be processed in real time that would be prohibitively complex and costly to deliver on a traditional centralized Cloud system. We propose a lightweight, energy-efficient, distributed, adaptive, multiple-context perspective event reasoning model under uncertainty on each IoT device (sensor/actuator). Each device senses and processes context data and infers events based on different local context perspectives: (i) expert knowledge on event representation, (ii) outliers inference, and (iii) deviation from locally predicted context. Such novel approximate reasoning paradigm is achieved through a contextualized, collaborative belief-driven clustering process, where clusters of devices are formed according to their belief on the presence of events. Our distributed and federated intelligence model efficiently identifies any localized abnormality on the contextual data in light of event reasoning through aggregating local degrees of belief, updates, and adjusts its knowledge to contextual data outliers and novelty detection. We provide comprehensive experimental and comparison assessment of our model over real contextual data with other localized and centralized event detection models and show the benefits stemmed from its adoption by achieving up to three orders of magnitude less energy consumption and high quality of inference
Hardware emulation of stochastic p-bits for invertible logic
The common feature of nearly all logic and memory devices is that they make
use of stable units to represent 0's and 1's. A completely different paradigm
is based on three-terminal stochastic units which could be called "p-bits",
where the output is a random telegraphic signal continuously fluctuating
between 0 and 1 with a tunable mean. p-bits can be interconnected to receive
weighted contributions from others in a network, and these weighted
contributions can be chosen to not only solve problems of optimization and
inference but also to implement precise Boolean functions in an inverted mode.
This inverted operation of Boolean gates is particularly striking: They provide
inputs consistent to a given output along with unique outputs to a given set of
inputs. The existing demonstrations of accurate invertible logic are
intriguing, but will these striking properties observed in computer simulations
carry over to hardware implementations? This paper uses individual micro
controllers to emulate p-bits, and we present results for a 4-bit ripple carry
adder with 48 p-bits and a 4-bit multiplier with 46 p-bits working in inverted
mode as a factorizer. Our results constitute a first step towards implementing
p-bits with nano devices, like stochastic Magnetic Tunnel Junctions
SCONNA: A Stochastic Computing Based Optical Accelerator for Ultra-Fast, Energy-Efficient Inference of Integer-Quantized CNNs
The acceleration of a CNN inference task uses convolution operations that are
typically transformed into vector-dot-product (VDP) operations. Several
photonic microring resonators (MRRs) based hardware architectures have been
proposed to accelerate integer-quantized CNNs with remarkably higher throughput
and energy efficiency compared to their electronic counterparts. However, the
existing photonic MRR-based analog accelerators exhibit a very strong trade-off
between the achievable input/weight precision and VDP operation size, which
severely restricts their achievable VDP operation size for the quantized
input/weight precision of 4 bits and higher. The restricted VDP operation size
ultimately suppresses computing throughput to severely diminish the achievable
performance benefits. To address this shortcoming, we for the first time
present a merger of stochastic computing and MRR-based CNN accelerators. To
leverage the innate precision flexibility of stochastic computing, we invent an
MRR-based optical stochastic multiplier (OSM). We employ multiple OSMs in a
cascaded manner using dense wavelength division multiplexing, to forge a novel
Stochastic Computing based Optical Neural Network Accelerator (SCONNA). SCONNA
achieves significantly high throughput and energy efficiency for accelerating
inferences of high-precision quantized CNNs. Our evaluation for the inference
of four modern CNNs at 8-bit input/weight precision indicates that SCONNA
provides improvements of up to 66.5x, 90x, and 91x in frames-per-second (FPS),
FPS/W and FPS/W/mm2, respectively, on average over two photonic MRR-based
analog CNN accelerators from prior work, with Top-1 accuracy drop of only up to
0.4% for large CNNs and up to 1.5% for small CNNs. We developed a
transaction-level, event-driven python-based simulator for the evaluation of
SCONNA and other accelerators (https://github.com/uky-UCAT/SC_ONN_SIM.git).Comment: To Appear at IPDPS 202
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