XBioSiP: A Methodology for Approximate Bio-Signal Processing at the Edge

Bio-signals exhibit high redundancy, and the algorithms for their processing are inherently error resilient. This property can be leveraged to improve the energy-efficiency of IoT-Edge (wearables) through the emerging trend of approximate computing. This paper presents XBioSiP, a novel methodology for approximate bio-signal processing that employs two quality evaluation stages, during the pre-processing and bio-signal processing stages, to determine the approximation parameters. It thereby achieves high energy savings while satisfying the user-determined quality constraint. Our methodology achieves, up to 19x and 22x reduction in the energy consumption of a QRS peak detection algorithm for 0% and <1% loss in peak detection accuracy, respectively.Comment: Accepted for publication at the Design Automation Conference 2019 (DAC'19), Las Vegas, Nevada, US

Approximate computing design exploration through data lifetime metrics

When designing an approximate computing system, the selection of the resources to modify is key. It is important that the error introduced in the system remains reasonable, but the size of the design exploration space can make this extremely difficult. In this paper, we propose to exploit a new metric for this selection: data lifetime. The concept comes from the field of reliability, where it can guide selective hardening: the more often a resource handles "live" data, the more critical it be-comes, the more important it will be to protect it. In this paper, we propose to use this same metric in a new way: identify the less critical resources as approximation targets in order to minimize the impact on the global system behavior and there-fore decrease the impact of approximation while increasing gains on other criteria

Application-Driven Synthesis of Energy-Efficient Reconfigurable-Precision Operators

The increasing performance demands in emerging Internet of Things applications clash with the low energy budgets of end-nodes. Therefore, hardware operators able to reconfigure their computational precision at runtime are increasingly employed in these devices, to obtain good-enough results at minimal energy costs. Among the many methods proposed to implement such operators, Dynamic Voltage and Accuracy Scaling (DVAS) is particularly promising, due to its broad applicability and low overheads. However, a straight-forward application of DVAS conflicts with the optimizations performed by classic EDA algorithms, and does not yield the expected results. In this paper, we propose a novel synthesis algorithm for reconfigurable-precision circuits, that allows to integrate DVAS in a standard implementation flow. Moreover, we show how this algorithm can exploit information about the application, namely on the frequency of usage of each precision, to further reduce the total energy consumption. Applying our method to the popular LeNet neural network for digit recognition, we are able to reduce the energy due to Multiply-And-Accumulate (MAC) operations by 25%, compared to a straight-forward application of DVAS

Compressor based approximate multiplier architectures for media processing applications

Approximate computing is an attractive technique to gain substantial improvement in the area, speed, and power in applications where exact computation is not required. This paper proposes two improved multiplier designs based on a new 4:2 approximate compressor circuit to simplify the hardware at the partial product reduction stage. The proposed multiplier designs are targeted towards error-tolerant applications. Exhaustive error and hardware analysis has been carried out on the existing and proposed multiplier designs. The results prove that the proposed approximate multiplier architecture performs better than the existing architectures without significant compromise on quality metrics. Experimental results show that die-area and power consumed are reduced upto 28%, and 25.29% respectively in comparison with the existing designs without significant compromise on accuracy