Approximate multipliers attract a large interest in the scientific literature that proposes several circuits built with approximate 4-2 compressors. Due to the large number of proposed solutions, the designer who wishes to use an approximate 4-2 compressor is faced with the problem of selecting the right topology. In this paper, we present a comprehensive survey and comparison of approximate 4-2 compressors previously proposed in literature. We present also a novel approximate compressor, so that a total of twelve different approximate 4-2 compressors are analyzed. The investigated circuits are employed to design 8x 8 and 16x 16 multipliers, implemented in 28nm CMOS technology. For each operand size we analyze two multiplier configurations, with different levels of approximations, both signed and unsigned. Our study highlights that there is no unique winning approximate compressor topology since the best solution depends on the required precision, on the signedness of the multiplier and on the considered error metric.

Recent trends in deep learning (DL) have made hardware accelerators essential for various high-performance computing (HPC) applications, including image classification, computer vision, and speech recognition. This survey summarizes and classifies the most recent developments in DL accelerators, focusing on their role in meeting the performance demands of HPC applications. We explore cutting-edge approaches to DL acceleration, covering not only GPU- and TPU-based platforms but also specialized hardware such as FPGA- and ASIC-based accelerators, Neural Processing Units, open hardware RISC-V-based accelerators, and co-processors. This survey also describes accelerators leveraging emerging memory technologies and computing paradigms, including 3D-stacked Processor-In-Memory, non-volatile memories like Resistive RAM and Phase Change Memories used for in-memory computing, as well as Neuromorphic Processing Units, and Multi-Chip Module-based accelerators. Furthermore, we provide insights into emerging quantum-based accelerators and photonics. Finally, this survey categorizes the most influential architectures and technologies from recent years, offering readers a comprehensive perspective on the rapidly evolving field of deep learning acceleration.

Truncated multipliers compute the n most-significant bits of the n × n bits product. This paper focuses on variable-correction truncated multipliers, where some partial-products are discarded, to reduce complexity, and a suitable compensation function is added to partly compensate the introduced error. The optimal compensation function, that minimizes the mean square error, is obtained in this paper in closed-form for the first time. A sub optimal compensation function, best suited for hardware implementation, is introduced. Efficient multipliers implementation based on sub-optimal function is discussed. Proposed truncated multipliers are extensively compared with previously proposed circuits. Experimental results, for a 0.18 μm technology, are also presented.

A new sense-amplifier-based flip-flop is presented. The output latch of the proposed circuit can be considered as an hybrid solution between the standard NAND-based set/reset latch and the NC-/sup 2/MOS approach. The proposed flip-flop provides ratioless design, reduced short-circuit power dissipation, and glitch-free operation. The simulation results, obtained for a 0.25-/spl mu/m technology, show improvements in the clock-to-output delay and the power dissipation with respect to the recently proposed high-speed flip-flops. The new circuit has been successfully employed in a high-speed direct digital frequency synthesizer chip, highlighting the effectiveness of the proposed flip-flop in high-speed standard cell-based applications.

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 using static segmentation. In these circuits a set of $m$ contiguous bits (a segment of $m$ bits) is extracted from each of the two $n$ -bits operand, the two segments are in input to a small $m\times m$ internal multiplier whose output is suitably shifted to obtain the result. We investigate both signed and unsigned multipliers, and for the latter we propose a new segmentation approach. We also present simple and effective correction techniques that can significantly reduce the approximation error with reduced hardware costs. We perform a detailed comparison with previously proposed approximate multipliers, considering a hardware implementation in 28 nm technology. The comparison shows that static segmented multipliers with the proposed correction technique have the desirable characteristic of being on (or close to) the Pareto-optimal frontier for both power vs normalized mean error distance and power vs mean relative error distance trade-off plots. These multipliers, therefore, are promising candidates for applications where their error performance is acceptable. This is confirmed by the results obtained for image processing and image classification applications.