EIE: Efficient Inference Engine on Compressed Deep Neural Network

State-of-the-art deep neural networks (DNNs) have hundreds of millions of connections and are both computationally and memory intensive, making them difficult to deploy on embedded systems with limited hardware resources and power budgets. While custom hardware helps the computation, fetching weights from DRAM is two orders of magnitude more expensive than ALU operations, and dominates the required power. Previously proposed 'Deep Compression' makes it possible to fit large DNNs (AlexNet and VGGNet) fully in on-chip SRAM. This compression is achieved by pruning the redundant connections and having multiple connections share the same weight. We propose an energy efficient inference engine (EIE) that performs inference on this compressed network model and accelerates the resulting sparse matrix-vector multiplication with weight sharing. Going from DRAM to SRAM gives EIE 120x energy saving; Exploiting sparsity saves 10x; Weight sharing gives 8x; Skipping zero activations from ReLU saves another 3x. Evaluated on nine DNN benchmarks, EIE is 189x and 13x faster when compared to CPU and GPU implementations of the same DNN without compression. EIE has a processing power of 102GOPS/s working directly on a compressed network, corresponding to 3TOPS/s on an uncompressed network, and processes FC layers of AlexNet at 1.88x10^4 frames/sec with a power dissipation of only 600mW. It is 24,000x and 3,400x more energy efficient than a CPU and GPU respectively. Compared with DaDianNao, EIE has 2.9x, 19x and 3x better throughput, energy efficiency and area efficiency.Comment: External Links: TheNextPlatform: http://goo.gl/f7qX0L ; O'Reilly: https://goo.gl/Id1HNT ; Hacker News: https://goo.gl/KM72SV ; Embedded-vision: http://goo.gl/joQNg8 ; Talk at NVIDIA GTC'16: http://goo.gl/6wJYvn ; Talk at Embedded Vision Summit: https://goo.gl/7abFNe ; Talk at Stanford University: https://goo.gl/6lwuer. Published as a conference paper in ISCA 201

N6-methyladenosine methylation analysis of long noncoding RNAs and mRNAs in 5-FU-resistant colon cancer cells

ABSTRACTN6 methyladenosine (m6A), methylation at the sixth N atom of adenosine, is the most common and abundant modification in mammalian mRNAs and non-coding RNAs. Increasing evidence shows that the alteration of m6A modification level could regulate tumour proliferation, metastasis, self-renewal, and immune infiltration by regulating the related expression of tumour genes. However, the role of m6A modification in colorectal cancer (CRC) drug resistance is unclear. Here, MeRIP-seq and RNA-seq techniques were utilized to obtain mRNA, lncRNA expression, and their methylation profiles in 5-Fluorouracil (5-FU)-resistant colon cancer HCT-15 cells and control cells. In addition, we performed detailed bioinformatics analysis as well as in vitro experiments of lncRNA to explore the function of lncRNA with differential m6A in CRC progression and drug resistance. In this study, we obtained the m6A methylomic landscape of CRC cells and resistance group cells by MeRIP-seq and RNA-seq. We identified 3698 differential m6A peaks, of which 2224 were hypermethylated, and 1474 were hypomethylated. Among the lncRNAs, 60 were hypermethylated, and 38 were hypomethylated. GO and KEGG analysis annotations showed significant enrichment of endocytosis and MAPK signalling pathways. Moreover, knockdown of lncRNA ADIRF-AS1 and AL139035.1 promoted CRC proliferation and invasive metastasis in vitro. lncRNA- mRNA network showed that ADIRF-AS1 and AL139035.1 May play a key role in regulating drug resistance formation. We provide the first m6A methylation profile in 5-FU resistance CRC cells and analyse the functions of differential m6A-modified mRNAs and lncRNAs. Our results indicated that differential m6A RNAs were significantly associated with MAPK signalling and endocytosis after induction of 5-FU resistance. Knockdown of LncRNA ADIRF-AS1 and AL139035.1 promotes CRC progression and might be critical in regulating drug resistance formation