Incremental criticality and yield gradients
Jinjun Xiong, Vladimir Zolotov, et al.
DATE 2008
As the trends driven by Moore's law come to an end, increased heterogeneity at all levels of computing is required to deliver the computing performance needed for emerging applications, leading to the proliferation of various application- or domain-specific accelerators. This in turn demands more memory bandwidth, as heterogeneous computing with accelerators consumes data at a much higher rate than traditional homogeneous computing, limiting the computing performance. To tackle this challenge, this article presents a conceptual near-memory acceleration architecture; demonstrates its practicality and plausibility using a recent experimental platform from IBM, as well as its potential impact on performance and energy efficiency; and discusses the need for adopting a high-level synthesis approach for such a near-memory acceleration architecture. Subsequently, this article concludes with future research directions for broad adoption of near-memory acceleration.
Jinjun Xiong, Vladimir Zolotov, et al.
DATE 2008
Seung Won Min, Sitao Huang, et al.
FPL 2019
Lerong Cheng, Jinjun Xiong, et al.
ASP-DAC 2008
Jinjun Xiong, Chandu Visweswariah, et al.
DAC 2009