24 research outputs found
Effective City Planning: A Data Driven Analysis of Infrastructure and Citizen Feedback in Bangalore
Leveraging civic data, divided into 3 categories spending, infrastructure and
citizen feedback, can present a clear picture of the priorities, performance,
and pain-points of a city. Data driven insights highlight the current issues
faced by citizens as well as disparity between government spending and quality
of work, and can aid in providing effective solutions. City infrastructure;
footpaths, lighting, and parks, describe the living quality of citizens and can
be compared to the annual spending in these sectors to track effectiveness.
Analyzing complaints ensures citizen feedback is taken into account during both
long-term planning and in short-term solutions to pinpoint critical areas of
improvement. Integrating an analysis loop and data driven dashboards can help
in improving performance of municipal corporations, while adding transparency
between citizens and the city officials. In the paper, constituency rankings
across the city infrastructure indicated a low importance towards greenery in
terms of Parks, where each constituency has less than 2% of their area as a
park. As populations in these areas are already high and increasing, this is
likely to worsen in the coming years. Comparing the results with complaints,
surprisingly the rankings of footpaths in constituencies were contrary to the
number of complaints in these constituencies, with high ranking constituencies
receiving the highest number of complaints, which would require further
analysis. In terms of street lights, the areas with low quality lighting were
associated with a large number of complaints from citizens, indicating that
action needs to be taken immediately. Overall, a text analysis of complaints
across constituencies reflected the everyday struggles of the city with the top
keywords 'roads' and 'vehicles', followed by 'footpaths' and 'garbage', which
are both critical problems in Bangalore City today.Comment: 5 pages, Technical Article, Report originally written in 201
Automatic Task Parallelization of Dataflow Graphs in ML/DL models
Several methods exist today to accelerate Machine Learning(ML) or
Deep-Learning(DL) model performance for training and inference. However, modern
techniques that rely on various graph and operator parallelism methodologies
rely on search space optimizations which are costly in terms of power and
hardware usage. Especially in the case of inference, when the batch size is 1
and execution is on CPUs or for power-constrained edge devices, current
techniques can become costly, complicated or inapplicable. To ameliorate this,
we present a Critical-Path-based Linear Clustering approach to exploit inherent
parallel paths in ML dataflow graphs. Our task parallelization approach further
optimizes the structure of graphs via cloning and prunes them via constant
propagation and dead-code elimination. Contrary to other work, we generate
readable and executable parallel Pytorch+Python code from input ML models in
ONNX format via a new tool that we have built called {\bf Ramiel}. This allows
us to benefit from other downstream acceleration techniques like intra-op
parallelism and potentially pipeline parallelism. Our preliminary results on
several ML graphs demonstrate up to 1.9 speedup over serial execution
and outperform some of the current mechanisms in both compile and runtimes.
Lastly, our methods are lightweight and fast enough so that they can be used
effectively for power and resource-constrained devices, while still enabling
downstream optimizations
Event-Driven Contrastive Divergence for Spiking Neuromorphic Systems
Restricted Boltzmann Machines (RBMs) and Deep Belief Networks have been
demonstrated to perform efficiently in a variety of applications, such as
dimensionality reduction, feature learning, and classification. Their
implementation on neuromorphic hardware platforms emulating large-scale
networks of spiking neurons can have significant advantages from the
perspectives of scalability, power dissipation and real-time interfacing with
the environment. However the traditional RBM architecture and the commonly used
training algorithm known as Contrastive Divergence (CD) are based on discrete
updates and exact arithmetics which do not directly map onto a dynamical neural
substrate. Here, we present an event-driven variation of CD to train a RBM
constructed with Integrate & Fire (I&F) neurons, that is constrained by the
limitations of existing and near future neuromorphic hardware platforms. Our
strategy is based on neural sampling, which allows us to synthesize a spiking
neural network that samples from a target Boltzmann distribution. The recurrent
activity of the network replaces the discrete steps of the CD algorithm, while
Spike Time Dependent Plasticity (STDP) carries out the weight updates in an
online, asynchronous fashion. We demonstrate our approach by training an RBM
composed of leaky I&F neurons with STDP synapses to learn a generative model of
the MNIST hand-written digit dataset, and by testing it in recognition,
generation and cue integration tasks. Our results contribute to a machine
learning-driven approach for synthesizing networks of spiking neurons capable
of carrying out practical, high-level functionality.Comment: (Under review
Gibbs Sampling with Low-Power Spiking Digital Neurons
Restricted Boltzmann Machines and Deep Belief Networks have been successfully
used in a wide variety of applications including image classification and
speech recognition. Inference and learning in these algorithms uses a Markov
Chain Monte Carlo procedure called Gibbs sampling. A sigmoidal function forms
the kernel of this sampler which can be realized from the firing statistics of
noisy integrate-and-fire neurons on a neuromorphic VLSI substrate. This paper
demonstrates such an implementation on an array of digital spiking neurons with
stochastic leak and threshold properties for inference tasks and presents some
key performance metrics for such a hardware-based sampler in both the
generative and discriminative contexts.Comment: Accepted at ISCAS 201