Increasing AI Energy Efficiency With Compute In Memory

Skyrocketing AI compute workloads and fixed power budgets are forcing chip and system architects to take a much harder look at compute in memory (CIM), which until recently was considered little more than a science project. CIM solves two problems. First, it takes more energy to move data back and forth between memory and processor than to actually process it. And second, there is so much da... » read more

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