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Yogesh Chobe

dblp:97/3395 · DBLP profile ↗
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2ranked-venue papers
0as first author
0since 2021 · last 2016
—ORCID · none

Domains — the database's venue-derived domains; a paper can count in several

Systems, architecture and hardware · 2

Expertise — from the expertise taxonomy: the topics of the expert's papers under the CCF categories. A weight counts papers with recency: 1 for a paper about the topic, 0.3 when the topic is its context, halved every five years.

Computer architecture, parallel and distributed computing, and storage systems
1 paper
Embedded and real-time systems · 56% Reconfigurable computing and FPGAs · 44%

Topics — the 3 heaviest of 4, each with the papers that count most for it

TopicWeightPapersLastEvidence papers
Reconfigurable computing and FPGAs
FPGA SoC
0.212016
SDSoC: A Higher-level Programming Environment for Zynq SoC and Ultrascale+ MPSoC · FPGA 2016
Embedded and real-time systems › embedded hardware platform › MPSoC
heterogeneous MPSoC
0.212016
SDSoC: A Higher-level Programming Environment for Zynq SoC and Ultrascale+ MPSoC · FPGA 2016
Embedded and real-time systems › embedded hardware platform
system-on-chip platform
0.112016
SDSoC: A Higher-level Programming Environment for Zynq SoC and Ultrascale+ MPSoC · FPGA 2016
YearPublicationVenuePosition
2016 SDSoC: A Higher-level Programming Environment for Zynq SoC and Ultrascale+ MPSoC
abstract
Zynq-7000 All Programmable SoC and the new Zynq Ultrascale+ MPSoC provide proven alternatives to traditional domain-specific application SoCs and enable extensive system-level differentiation, integration and flexibility through hardware, software and I/O programmability.
Vinod Kathail, Welson Sun, Yogesh Chobe, Tom Shui, Jorge Carrillo
FPGA4
2006 Compiler optimization of embedded applications for an adaptive SoC architecture
abstract
Adaptive Explicitly Parallel Instruction Computing (AEPIC) is a stylized form of a reconfigurable system-on-a-chip that is designed to enable compiler control of reconfigurable resources. In this paper, and for the first time, we validate the viability of automating two key optimizations proposed in the AEPIC compilation framework: configuration allocation and configuration scheduling.The AEPIC architecture is comprised of an Explicitly Parallel Instruction Computing (EPIC) core coupled with an adaptive fabric and architectural features to support dynamic management of the fabric. We show that this approach to compiler-centric hardware customization, originally proposed by Palem, Talla, Devaney and Wong ([26],[27]), yields speedups with factors from 150% to over 600% for embedded applications, when compared with general purpose and digital signal processor solutions. We also provide a normalized cost analysis for our performance gains, where the normalization is based on the area of silicon required. In addition, we provide an analysis of the AEPIC architectural space, where we identify the "sweet-spot" of performance on the AEPIC architecture by examining the performance across benchmarks and computational resource configurations. Finally, we have a preliminary result for how our compiler-based approach impacts productivity metrics in the development of hardware/software partitioned custom solutions. Our implementation and validation platform is based on the well-known TRIMARAN optimizing compiler infrastructure [13].
Charles Hardnett, Krishna V. Palem, Yogesh Chobe
CASES3