Approximate Computing (AxC) paradigm aims at designing computing systems that can satisfy the rising performance demands and improve the energy efficiency. AxC exploits the gap between the level of accuracy required by the users, and the actual precision provided by the computing system, for achieving diverse optimizations. Various AxC techniques have been proposed so far in the literature at different abstraction levels from hardware to software. These techniques have been successfully utilized and combined to realize approximate implementations of applications in various domains (e.g. data analytic, scientific computing, multimedia and signal processing, and machine learning). Unfortunately, state-of-the-art approximation methodologies focus on a single abstraction level, such as combining elementary components (e.g., arithmetic operations) which are firstly approximated using component-level metrics and then combined to provide a good trade-off between efficiency and accuracy at the application level. This hinders the possibility for designers to explore different approximation opportunities, optimized for different applications and implementation targets. Therefore, we designed and implemented E- \mathbb IDEA, an automatic framework that provides an application-driven approximation approach to find the best approximate versions of a given application targeting different implementations (i.e., hardware and software). E- \mathbb IDEA compounds 1) a source-to-source manipulation tool and 2) an evolutionary search engine to automatically realize approximate application variants and perform a Design-Space Exploration (DSE). The latter results in a set of non-dominate approximate solutions in terms of trade-off between accuracy and efficiency. Experimental results validate the effectiveness and the flexibility of the approach in generating optimized approximate implementations of different applications, by using different approximation techniques and different accuracy/error metrics and for different implementation targets.

So far, multiple classifier systems have been increasingly designed to take advantage of hardware features, such as high parallelism and computational power. Indeed, compared to software implementations, hardware accelerators guarantee higher throughput and lower latency. Although the combination of multiple classifiers leads to high classification accuracy, the required area overhead makes the design of a hardware accelerator unfeasible, hindering the adoption of commercial configurable devices. For this reason, in this paper, we exploit approximate computing design paradigm to trade hardware area overhead off for classification accuracy. In particular, starting from trained DT models and employing precision-scaling technique, we explore approximate decision tree variants by means of multiple objective optimization problem, demonstrating a significant performance improvement targeting field-programmable gate array devices.

As modern applications demand an unprecedented level of computational resources, traditional computing system design paradigms are no longer adequate to guarantee significant performance enhancement at an affordable cost. Approximate Computing (AxC) has been introduced as a potential candidate to achieve better computational performances by relaxing non-critical functional system specifications. In this article, we propose a systematic and high-abstraction-level approach allowing the automatic generation of near Pareto-optimal approximate configurations for a Discrete Cosine Transform (DCT) hardware accelerator. We obtain the approximate variants by using approximate operations, having configurable approximation degree, rather than full-precise ones. We use a genetic searching algorithm to find the appropriate tuning of the approximation degree, leading to optimal tradeoffs between accuracy and gains. Finally, to evaluate the actual HW gains, we synthesize non-dominated approximate DCT variants for two different target technologies, namely, Field Programmable Gate Arrays (FPGAs) and Application Specific Integrated Circuits (ASICs). Experimental results show that the proposed approach allows performing a meaningful exploration of the design space to find the best tradeoffs in a reasonable time. Indeed, compared to the state-of-the-art work on approximate DCT, the proposed approach allows an 18% average energy improvement while providing at the same time image quality improvement.

As computational demand and energy efficiency of computer systems are becoming increasingly relevant requirements, traditional design paradigms are bound to become no longer appropriate, as they cannot guarantee significant improvements. The approximate-computing design paradigm has been introduced as a potential candidate to achieve better performances, by relaxing non-critical functional specifications. Anyway, several challenges need to be addressed in order to exploit its potential. In this paper, we propose a systematic and application-independent approximate design approach suitable to combinational logic circuits. Our approach is based on non-trivial local rewriting of and-inverter graphs (AIG), reducing the number of AIG-nodes and possibly resulting in lower hardware resources requirements. We adopt multi-objective optimization to carefully introduce approximation while aiming at optimal trade-offs between error and hardware-requirements. We evaluate our approach using different benchmarks, and, in order to measure actual gains, we perform actual synthesis of Pareto-optimal approximate configurations. Experimental results show that the proposed approach allows achieving significant savings, since resulting approximate circuits exhibit lower requirements and restrained error w.r.t. their exact counterparts.

In the last years, agile methodologies are gaining substantial momentum, becoming increasingly popular in a broad plethora of industrial contexts. Unfortunately, many obstacles have been met while pursuing adoption in secure and safe systems, where different standards and operational constraints apply. In this paper, we propose a novel agile methodology for the development and innovation of safety-critical systems. In particular, we developed an extension of the well-known Scrum methodology and discussed the complete workflow. We finally validated the applicability of the proposed methodology over a real case study from the railway domain.