2 research outputs found
Interstellar: Using Halide's Scheduling Language to Analyze DNN Accelerators
We show that DNN accelerator micro-architectures and their program mappings
represent specific choices of loop order and hardware parallelism for computing
the seven nested loops of DNNs, which enables us to create a formal taxonomy of
all existing dense DNN accelerators. Surprisingly, the loop transformations
needed to create these hardware variants can be precisely and concisely
represented by Halide's scheduling language. By modifying the Halide compiler
to generate hardware, we create a system that can fairly compare these prior
accelerators. As long as proper loop blocking schemes are used, and the
hardware can support mapping replicated loops, many different hardware
dataflows yield similar energy efficiency with good performance. This is
because the loop blocking can ensure that most data references stay on-chip
with good locality and the processing units have high resource utilization. How
resources are allocated, especially in the memory system, has a large impact on
energy and performance. By optimizing hardware resource allocation while
keeping throughput constant, we achieve up to 4.2X energy improvement for
Convolutional Neural Networks (CNNs), 1.6X and 1.8X improvement for Long
Short-Term Memories (LSTMs) and multi-layer perceptrons (MLPs), respectively.Comment: Published as a conference paper at ASPLOS 202
Multiple novel prostate cancer susceptibility signals identified by fine-mapping of known risk loci among Europeans
Genome-wide association studies (GWAS) have identified numerous common prostate cancer (PrCa) susceptibility loci. We have
fine-mapped 64 GWAS regions known at the conclusion of the iCOGS study using large-scale genotyping and imputation in
25 723 PrCa cases and 26 274 controls of European ancestry. We detected evidence for multiple independent signals at 16
regions, 12 of which contained additional newly identified significant associations. A single signal comprising a spectrum of
correlated variation was observed at 39 regions; 35 of which are now described by a novel more significantly associated lead SNP,
while the originally reported variant remained as the lead SNP only in 4 regions. We also confirmed two association signals in
Europeans that had been previously reported only in East-Asian GWAS. Based on statistical evidence and linkage disequilibrium
(LD) structure, we have curated and narrowed down the list of the most likely candidate causal variants for each region.
Functional annotation using data from ENCODE filtered for PrCa cell lines and eQTL analysis demonstrated significant
enrichment for overlap with bio-features within this set. By incorporating the novel risk variants identified here alongside the
refined data for existing association signals, we estimate that these loci now explain ∼38.9% of the familial relative risk of PrCa,
an 8.9% improvement over the previously reported GWAS tag SNPs. This suggests that a significant fraction of the heritability of
PrCa may have been hidden during the discovery phase of GWAS, in particular due to the presence of multiple independent
signals within the same regio