Design Space Exploration of Approximate Computing Techniques

Nowadays, the rising energy consumption of smartphones and portable devices creates an energy efficiency challenge. To address this problem, Approximate Computing (AxC) techniques are becoming popular since they sacrifice computation accuracy for enhanced performance, energy efficiency, and area reduction. However, selecting suitable AxC techniques for target applications remains intricate. Design Space Exploration (DSE) approaches can be employed to systematically explore all different possible approximate versions of an application and select the most suitable versions. This paper proposes a DSE approach that models the target application computations and the approximation-induced errors using Interval Arithmetic. The experimental results show the efficiency of the proposed approach in quickly evaluating different approximate versions of an application eliminating the time-consuming task of executing each approximate version. Also, using Artificial intelligence, such as Reinforcement Learning approaches, is proposed to explore the design space automatically

Prediction of the Impact of Approximate Computing on Spiking Neural Networks via Interval Arithmetic

Approximate Computing (AxC) techniques allow trade-off accuracy for performance, energy, and area reduction gains. One of the applications suitable for using AxC techniques are the Spiking Neural Networks (SNNs). SNNs are the new frontier for artificial intelligence since they allow for a more reliable hardware design. Unfortunately, this design requires some area minimization strategies when the target hardware reaches the edge of computing. In this work, we first extract the computation flow of an SNN, then employ Interval Arithmetic (IA) to model the propagation of the approximation error. This enables a quick evaluation of the impact of approximation. Experimental results confirm the model’s adherence and the capability of reducing the exploration time

Spiking Neural Network Data Reduction via Interval Arithmetic

Approximate Computing (AxC) allows reducing the accuracy required by the user and the precision provided by the computing system to optimize the whole system in terms of performance, energy, and area reduction. Spiking Neural Networks(SNNs) are the new frontier for artificial intelligence because they better represent the timing influence on decision making, and also allow for a more reliable hardware design. Unfortunately, this design requires some area minimization strategies when the target hardware reaches the edge of computing. This seminal work introduces modeling of the approximation for data storage that supports an SNN via Interval Arithmetic (IA) by extracting the computation graph of the SNN and then resorting to IA to quickly evaluate the impact of approximation in terms of loss inaccuracy without executing the network each time. Experimental results comparing our model to the real network confirm the quality of the approach

Design Space Exploration of Approximate Computing Techniques with a Reinforcement Learning Approach

Approximate Computing (AxC) techniques have become increasingly popular in trading off accuracy for performance gains in various applications. Selecting the best AxC techniques for a given application is challenging. Among proposed approaches for exploring the design space, Machine Learning approaches such as Reinforcement Learning (RL) show promising results. In this paper, we proposed an RL-based multi-objective Design Space Exploration strategy to find the approximate versions of the application that balance accuracy degradation and power and computation time reduction. Our experimental results show a good trade-off between accuracy degradation and decreased power and computation time for some benchmarks

Design Space Exploration of Approximate Computing Techniques

Nowadays, the rising energy consumption of smartphones and portable devices creates an energy efficiency challenge. To address this problem, Approximate Computing (AxC) techniques are becoming popular since they sacrifice computation accuracy for enhanced performance, energy efficiency, and area reduction. However, selecting suitable AxC techniques for target applications remains intricate. Design Space Exploration (DSE) approaches can be employed to systematically explore all different possible approximate versions of an application and select the most suitable versions. This paper proposes a DSE approach that models the target application computations and the approximation-induced errors using Interval Arithmetic. The experimental results show the efficiency of the proposed approach in quickly evaluating different approximate versions of an application eliminating the time-consuming task of executing each approximate version. Also, using Artificial intelligence, such as Reinforcement Learning approaches, is proposed to explore the design space automatically

Input-Aware Approximate Computing

In the last decade, Approximate Computing (AxC) has been extensively employed to improve the energy efficiency of computing systems, at different abstraction levels. The main AxC goal is reducing the energy budget used to execute error-tolerant applications, at the cost of a controlled and intrinsically-tolerable quality degradation. An important amount of work has been done in proposing approximate versions of basic operations, using fewer resources. From a hardware standpoint, several approximate arithmetic operations have been proposed. Although effective, such approximate hardware operators are not tailored to a specific final application. Thus, their effectiveness will depend on the actual application using them. Taking into account the target application and the related input data distribution, the final energy efficiency can be pushed further. In this paper we showcase the advantage of considering the data distribution by designing an input-aware approximate multiplier specifically intended for a high pass FIR filter, where the input distribution pattern for one operand is not uniform. Experimental results show that we can significantly reduce the power consumption while keeping an error rate lower than state of the art approximate multipliers