Nowadays, the number of emerging embedded systems rapidly grows in many application domains, due to recent advances in artificial intelligence and internet of things. The main inherent specification of these application-specific systems is that they have not a general nature and are basically developed to only perform a particular task and therefore, deal only with a limited and predefined range of custom input values. Despite this significant feature, these emerging applications are still conventionally implemented using general-purpose and precise digital computational blocks, which are essentially developed to provide the correct result for all possible input values. This highly degrades the physical properties of these applications while does not improve their functionality. To resolve this conflict, a novel computational paradigm named as partially-precise computing is introduced in this paper, based on an inspiration from the brain information reduction hypothesis as a tenet of neuroscience. The main specification of a Partially-Precise Computational (PPC) block is that it provides the precise result only for a desired, limited, and predefined set of input values. This relaxes its internal structure which results in improved physical properties with respect to a conventional precise block. The PPC blocks improve the implementation costs of the embedded applications, with a negligible or even without any output quality degradation with respect to the conventional implementation. The applicability and efficiency of the first instances of PPC adders and multipliers in a Gaussian denoising filter, an image blending and a face recognition neural network are demonstrated by means of a wide range of simulation and synthesis results.

翻译：当今，随着人工智能与物联网技术的快速发展，新兴嵌入式系统在众多应用领域的数量迅速增长。这类专用系统的主要固有特征在于其非通用性——它们本质上仅用于执行特定任务，因此只处理有限且预定义的自定义输入值范围。尽管具备这一显著特性，这些新兴应用仍沿用传统方式，采用本质上为所有可能输入值提供正确结果的通用精确数字计算模块进行实现。这种做法在无法改善功能性的同时，严重降低了应用的物理特性。为解决这一矛盾，本文受神经科学中大脑信息简化假说的启发，提出了一种名为“部分精确计算”的新型计算范式。部分精确计算（PPC）模块的核心特性在于：仅对所需、有限且预定义的输入值集合提供精确结果。这使其内部结构得以简化，从而在物理特性上优于传统精确模块。相较于传统实现方式，PPC模块在改善嵌入式应用实现成本的同时，仅导致可忽略甚至无输出质量退化。通过高斯降噪滤波器、图像混合及人脸识别神经网络中首批PPC加法器与乘法器的实例化，结合广泛的仿真与综合结果，验证了该范式的适用性与高效性。