中文 English

Algorithm HW Framework That Minimizes Accuracy Degradation, Data Movement, And Energy Consumption Of DNN Accelerators (Georgia Tech)


This new research paper titled "An Algorithm-Hardware Co-design Framework to Overcome Imperfections of Mixed-signal DNN Accelerators" was published by researchers at Georgia Tech. According to the paper's abstract, "In recent years, processing in memory (PIM) based mixed-signal designs have been proposed as energy- and area-efficient solutions with ultra high throughput to accelerate DNN com... » read more

Polynesia, A Novel Hardware/Software Cooperative Design for In-Memory HTAP Databases


A team of researchers from ETH Zurich, Google and Univ. of Illinois Urbana-Champaign recently published a technical paper titled "Polynesia: Enabling High-Performance and Energy-Efficient Hybrid Transactional/Analytical Databases with Hardware/Software Co-Design". Abstract (partial) "We propose Polynesia, a hardware–software co-designed system for in-memory HTAP [hybrid transactional/anal... » read more

Benchmarking Memory-Centric Computing Systems: Analysis of Real Processing-in-Memory Hardware


Abstract "Many modern workloads such as neural network inference and graph processing are fundamentally memory-bound. For such workloads, data movement between memory and CPU cores imposes a significant overhead in terms of both latency and energy. A major reason is that this communication happens through a narrow bus with high latency and limited bandwidth, and the low data reuse in memory-bo... » read more