14 research outputs found
Automated Circuit Approximation Method Driven by Data Distribution
We propose an application-tailored data-driven fully automated method for
functional approximation of combinational circuits. We demonstrate how an
application-level error metric such as the classification accuracy can be
translated to a component-level error metric needed for an efficient and fast
search in the space of approximate low-level components that are used in the
application. This is possible by employing a weighted mean error distance
(WMED) metric for steering the circuit approximation process which is conducted
by means of genetic programming. WMED introduces a set of weights (calculated
from the data distribution measured on a selected signal in a given
application) determining the importance of each input vector for the
approximation process. The method is evaluated using synthetic benchmarks and
application-specific approximate MAC (multiply-and-accumulate) units that are
designed to provide the best trade-offs between the classification accuracy and
power consumption of two image classifiers based on neural networks.Comment: Accepted for publication at Design, Automation and Test in Europe
(DATE 2019). Florence, Ital
Power Optimizations in MTJ-based Neural Networks through Stochastic Computing
Artificial Neural Networks (ANNs) have found widespread applications in tasks
such as pattern recognition and image classification. However, hardware
implementations of ANNs using conventional binary arithmetic units are
computationally expensive, energy-intensive and have large area overheads.
Stochastic Computing (SC) is an emerging paradigm which replaces these
conventional units with simple logic circuits and is particularly suitable for
fault-tolerant applications. Spintronic devices, such as Magnetic Tunnel
Junctions (MTJs), are capable of replacing CMOS in memory and logic circuits.
In this work, we propose an energy-efficient use of MTJs, which exhibit
probabilistic switching behavior, as Stochastic Number Generators (SNGs), which
forms the basis of our NN implementation in the SC domain. Further, error
resilient target applications of NNs allow us to introduce Approximate
Computing, a framework wherein accuracy of computations is traded-off for
substantial reductions in power consumption. We propose approximating the
synaptic weights in our MTJ-based NN implementation, in ways brought about by
properties of our MTJ-SNG, to achieve energy-efficiency. We design an algorithm
that can perform such approximations within a given error tolerance in a
single-layer NN in an optimal way owing to the convexity of the problem
formulation. We then use this algorithm and develop a heuristic approach for
approximating multi-layer NNs. To give a perspective of the effectiveness of
our approach, a 43% reduction in power consumption was obtained with less than
1% accuracy loss on a standard classification problem, with 26% being brought
about by the proposed algorithm.Comment: Accepted in the 2017 IEEE/ACM International Conference on Low Power
Electronics and Desig
Deep Learning Training with Simulated Approximate Multipliers
This paper presents by simulation how approximate multipliers can be utilized
to enhance the training performance of convolutional neural networks (CNNs).
Approximate multipliers have significantly better performance in terms of
speed, power, and area compared to exact multipliers. However, approximate
multipliers have an inaccuracy which is defined in terms of the Mean Relative
Error (MRE). To assess the applicability of approximate multipliers in
enhancing CNN training performance, a simulation for the impact of approximate
multipliers error on CNN training is presented. The paper demonstrates that
using approximate multipliers for CNN training can significantly enhance the
performance in terms of speed, power, and area at the cost of a small negative
impact on the achieved accuracy. Additionally, the paper proposes a hybrid
training method which mitigates this negative impact on the accuracy. Using the
proposed hybrid method, the training can start using approximate multipliers
then switches to exact multipliers for the last few epochs. Using this method,
the performance benefits of approximate multipliers in terms of speed, power,
and area can be attained for a large portion of the training stage. On the
other hand, the negative impact on the accuracy is diminished by using the
exact multipliers for the last epochs of training.Comment: Presented at: IEEE International Conference on Robotics and
Biomimetics (ROBIO) 2019, Dali, China, December 2019. WINNER OF THE MOZI BEST
PAPER IN AI AWAR
Fine-Grained Power Gated Multiplier with Online Calibration for Medical IoT Devices
With intensive research in the fields of machine learning and neural networks to improve its accuracy comes the responsibility to realize feasible hardware solutions on battery powered IoT devices. This work presents a study of analysis of power hungry computations and a fine-grained power gated multiplier design using approximation, that aims at energy optimization exploiting error resilience of these applications. We use truncation to reduce cycles and low power techniques to reduce power, thus achieving a 2-fold energy reduction. We use wearable IoT devices for medical purposes as our case study and show the generality of our work across applications. Our work performs similar to, or better than the latest work in the field and is a more generic implementation. We propose an online calibration mechanism to determine the approximation rate dynamically that maximizes energy optimization with very low accuracy loss. Our method uses a clustering solution to pre-determine the output label in a majority of cases, without having to need an inference model, thus further reducing energy. We achieve 78% energy improvement compared to a baseline implementation with just 0.46% accuracy loss across benchmarks
Genetic Improvement of Software (Dagstuhl Seminar 18052)
We document the program and the immediate outcomes of Dagstuhl Seminar 18052 “Genetic
Improvement of Software”. The seminar brought together researchers in Genetic Improvement
(GI) and related areas of software engineering to investigate what is achievable with current technology and the current impediments to progress and how GI can affect the software development
process. Several talks covered the state-of-the-art and work in progress. Seven emergent topics
have been identified ranging from the nature of the GI search space through benchmarking and
practical applications. The seminar has already resulted in multiple research paper publications.
Four by participants of the seminar will be presented at the GI workshop co-located with the
top conference in software engineering - ICSE. Several researchers started new collaborations,
results of which we hope to see in the near future
Approximate Computing Survey, Part II: Application-Specific & Architectural Approximation Techniques and Applications
The challenging deployment of compute-intensive applications from domains
such Artificial Intelligence (AI) and Digital Signal Processing (DSP), forces
the community of computing systems to explore new design approaches.
Approximate Computing appears as an emerging solution, allowing to tune the
quality of results in the design of a system in order to improve the energy
efficiency and/or performance. This radical paradigm shift has attracted
interest from both academia and industry, resulting in significant research on
approximation techniques and methodologies at different design layers (from
system down to integrated circuits). Motivated by the wide appeal of
Approximate Computing over the last 10 years, we conduct a two-part survey to
cover key aspects (e.g., terminology and applications) and review the
state-of-the art approximation techniques from all layers of the traditional
computing stack. In Part II of our survey, we classify and present the
technical details of application-specific and architectural approximation
techniques, which both target the design of resource-efficient
processors/accelerators & systems. Moreover, we present a detailed analysis of
the application spectrum of Approximate Computing and discuss open challenges
and future directions.Comment: Under Review at ACM Computing Survey