Using a unique microarray platform for cytosine methylation profiling, the DNA methylation landscape of the human genome was monitored at more than 21,000 sites, including 79% of the annotated transcriptional start sites (TSS). Analysis of an oligodendroglioma derived cell line LN-18 revealed more than 4000 methylated TSS. The gene-centric analysis indicated a complex pattern of DNA methylation exists along each autosome, with a trend of increasing density approaching the telomeres. Remarkably, 2% of CpG islands (CGI) were densely methylated, and 17% had significant levels of 5 mC, whether or not they corresponded to a TSS. Substantial independent verification, obtained from 95 loci, suggested that this approach is capable of large scale detection of cytosine methylation with an accuracy approaching 90%. In addition, we detected large genomic domains that are also susceptible to DNA methylation reinforced inactivation, such as the HOX cluster on chromosome 7 (CH7). Extrapolation from the data suggests that more than 2000 genomic loci may be susceptible to methylation and associated inactivation, and most have yet to be identified. Finally, we report six new targets of epigenetic inactivation (IRX3, WNT10A, WNT6, RARalpha, BMP7 and ZGPAT). These targets displayed cell line and tumor specific differential methylation when compared with normal brain samples, suggesting they may have utility as biomarkers. Uniquely, hypermethylation of the CGI within an IRX3 exon was correlated with over-expression of IRX3 in tumor tissues and cell lines relative to normal brain samples.

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Forecasts project that by 2020, there will be around 50 billion devices connected to the Internet of Things (IoT), most of which will operate untethered and unplugged. While environmental energy harvesting is a promising solution to power these IoT edge devices, it introduces new complexities due to the unreliable nature of ambient energy sources. In the presence of an unreliable power supply, frequent checkpointing of the system state becomes imperative, and recent research has proposed the concept of in-situ checkpointing by using ferroelectric RAM (FRAM), an emerging non-volatile memory technology, as unified memory in these systems. Even though an entirely FRAM-based solution provides reliability, it is energy inefficient compared to SRAM due to the higher access latency of FRAM. On the other hand, an entirely SRAM-based solution is highly energy efficient but is unreliable in the face of power loss. This paper advocates an intermediate approach in hybrid FRAM-SRAM microcontrollers that involves judicious memory mapping of program sections to retain the reliability benefits provided by FRAM while performing almost as efficiently as an SRAM-based system. We propose an energy-aware memory mapping technique that maps different program sections to the hybrid FRAM-SRAM microcontroller such that energy consumption is minimized without sacrificing reliability. Our technique consists of eM-map , which performs a one-time characterization to find the optimal memory map for the functions that constitute a program and energy-align , a novel hardware-software technique that aligns the system’s powered-on time intervals to function execution boundaries, which results in further improvements in energy efficiency and performance. Experimental results obtained using the MSP430FR5739 microcontroller demonstrate a significant performance improvement of up to 2x and energy reduction of up to 20% over a state-of-the-art FRAM-based solution. Finally, we present a case study that shows the implementation of our techniques in the context of a real IoT application.

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Transformers have transformed the field of natural language processing. This performance is largely attributed to the use of stacked "self-attention" layers, each of which consists of matrix multiplies as well as softmax operations. As a result, unlike other neural networks, the softmax operation accounts for a significant fraction of the total run-time of Transformers.To address this, we propose Softermax, a hardware-friendly softmax design. Softermax consists of base replacement, lowprecision softmax computations, and an online normalization calculation. We show Softermax results in 2.35x the energy efficiency at 0.90x the size of a comparable baseline, with negligible impact on network accuracy.

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