Deep Neural Networks (DNNs) are very popular because of their high performance in
various cognitive tasks in Machine Learning (ML). Recent advancements in DNNs have …

Computing systems at all scales (from mobile handheld devices to supercomputers, servers,
and large cloud-based data centers) have seen significant performance gains, mostly …

Given the stringent requirements of energy efficiency for Internet-of-Things edge devices,
approximate multipliers, as a basic component of many processors and accelerators, have …

Approximate computing is a new trend that trades off computational accuracy for lower
energy dissipation and design complexity in various applications, where high precision is …

Approximate multipliers are used in error-tolerant applications, sacrificing the accuracy of
results to minimize power or delay. In this paper we investigate approximate multipliers …

In this paper, the approximate parallel prefix adders with minimizing hardware and timing
complexities are proposed. Moreover, an approximate multiplier based on these adders is …

Approximate computing, frequently used in error tolerant applications, aims to achieve
higher circuit performances by allowing the possibility of inaccurate results, rather than …

We present an approximate logarithmic multiplier with two-stage operand trimming, which
prioritises area and energy consumption while retains acceptable accuracy. The multiplier …

Arithmetic plays a major role in a computer? s performance and efficiency. Building new
computing platforms supported by the traditional binary arithmetic and silicon-based …

As the demand for computational power increases drastically, traditional solutions to
address those needs struggle to keep up. Consequently, there has been a proliferation of …