Ultra-efficient processing in-memory for data intensive applications

Recent years have witnessed a rapid growth in the domain of Internet of Things (IoT). This network of billions of devices generates and exchanges huge amount of data. The limited cache capacity and memory bandwidth make transferring and processing such data on traditional CPUs and GPUs highly inefficient, both in terms of energy consumption and delay. However, many IoT applications are statistical at heart and can accept a part of inaccuracy in their computation. This enables the designers to reduce complexity of processing by approximating the results for a desired accuracy. In this paper, we propose an ultra-efficient approximate processing in-memory architecture, called APIM, which exploits the analog characteristics of non-volatile memories to support addition and multiplication inside the crossbar memory, while storing the data. The proposed design eliminates the overhead involved in transferring data to processor by virtually bringing the processor inside memory. APIM dynamically configures the precision of computation for each application in order to tune the level of accuracy during runtime. Our experimental evaluation running six general OpenCL applications shows that the proposed design achieves up to 20× performance improvement and provides 480× improvement in energy-delay product, ensuring acceptable quality of service. In exact mode, it achieves 28× energy savings and 4.8× speed up compared to the state-of-the-art GPU cores.