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Eric R. Hein

dblp:130/7671 · DBLP profile ↗
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5ranked-venue papers
0as first author
0since 2021 · last 2019
—ORCID · none

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

Systems, architecture and hardware · 5

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
4 papers
Hardware reliability and fault tolerance · 20% Energy-efficient computing · 19% Memory systems · 15%
Software engineering, system software, and programming languages
1 paper
Compilers and program optimization · 100%

Topics — the 16 heaviest of 16, each with the papers that count most for it

TopicWeightPapersLastEvidence papers
Hardware reliability and fault tolerance › error resilience
error-tolerant computing
0.312018
Extending Moore's Law via Computationally Error-Tolerant Computing · ACM Trans. Archit. Code Optim. 2018
Processor architecture and microarchitecture
instruction set architecture
0.312018
Extending Moore's Law via Computationally Error-Tolerant Computing · ACM Trans. Archit. Code Optim. 2018
Energy-efficient computing › low-power design
low-power processor design
0.312018
Extending Moore's Law via Computationally Error-Tolerant Computing · ACM Trans. Archit. Code Optim. 2018
Memory systems
memory hierarchy
0.312018
Memory System Design for Ultra Low Power, Computationally Error Resilient Processor Microarchitectures · HPCA 2018
Integrated circuit design
redundant number representation
0.312018
Extending Moore's Law via Computationally Error-Tolerant Computing · ACM Trans. Archit. Code Optim. 2018
Hardware reliability and fault tolerance › arithmetic codes
redundant residue number system
0.312018
Extending Moore's Law via Computationally Error-Tolerant Computing · ACM Trans. Archit. Code Optim. 2018
Parallel and multicore computing › parallel computing
parallel program analysis
0.212015
Contech: Efficiently Generating Dynamic Task Graphs for Arbitrary Parallel Programs · ACM Trans. Archit. Code Optim. 2015
Parallel and multicore computing
parallel programming environment
0.212015
Contech: Efficiently Generating Dynamic Task Graphs for Arbitrary Parallel Programs · ACM Trans. Archit. Code Optim. 2015
Memory systems
cache coherence
0.212013
High-speed formal verification of heterogeneous coherence hierarchies · HPCA 2013
Electronic design automation › hardware verification and test
formal verification
0.212013
High-speed formal verification of heterogeneous coherence hierarchies · HPCA 2013
Electronic design automation › hardware verification and test
hardware verification
0.212013
High-speed formal verification of heterogeneous coherence hierarchies · HPCA 2013
Energy-efficient computing › voltage scaling
near-threshold voltage operation
0.112018
Memory System Design for Ultra Low Power, Computationally Error Resilient Processor Microarchitectures · HPCA 2018
Energy-efficient computing › low-power design
ultra-low power computing
0.112018
Memory System Design for Ultra Low Power, Computationally Error Resilient Processor Microarchitectures · HPCA 2018
Energy-efficient computing
voltage scaling
0.112018
Extending Moore's Law via Computationally Error-Tolerant Computing · ACM Trans. Archit. Code Optim. 2018
Compilers and program optimization
compiler optimization
0.112015
Contech: Efficiently Generating Dynamic Task Graphs for Arbitrary Parallel Programs · ACM Trans. Archit. Code Optim. 2015
GPUs and heterogeneous computing › heterogeneous architecture
heterogeneous shared memory
0.012013
High-speed formal verification of heterogeneous coherence hierarchies · HPCA 2013

Methods — techniques the papers use, named apart from their topics

task graph representation · 0.4dynamic instrumentation · 0.4simulation · 0.3design space exploration · 0.3murphi model checking · 0.2manager-client pairing framework · 0.2
YearPublicationVenuePosition
2019 A microbenchmark characterization of the Emu chick
Jeffrey Young 0001, Eric R. Hein, Srinivas Eswar, Patrick Lavin, Jiajia Li 0001, E. Jason Riedy, Richard W. Vuduc, Thomas M. Conte
Parallel Comput.2
2018 Memory System Design for Ultra Low Power, Computationally Error Resilient Processor Microarchitectures
abstract
Dennard scaling ended a decade ago. Energy reduction by lowering supply voltage has been limited because of guard bands and a subthreshold slope of over 60mV/decade in MOSFETs. On the other hand, newly-proposed logic devices maintain a high on/off ratio for drain currents even at significantly lower operating voltages. However, such ultra low power technology would eventually suffer from intermittent errors in logic as a result of operating close to the thermal noise floor. Computational error correction mitigates this issue by efficiently correcting stochastic bit errors that may occur in computational logic operating at low signal energies, thereby allowing for energy reduction by lowering supply voltage to tens of millivolts. Cores based on a Redundant Residual Number System (RRNS), which represents a number using a tuple of smaller numbers, are a promising candidate for implementing energyefficient computational error correction. However, prior RRNS core microarchitectures abstract away the memory hierarchy and do not consider the power-performance impact of RNS-based memory addressing. When compared with a non-error-correcting core addressing memory in binary, naive RNS-based memory addressing schemes cause a slowdown of over 3x/2x for inorder/out-of-order cores respectively. In this paper, we analyze RNS-based memory access pattern behavior and provide solutions in the form of novel schemes and the resulting design space exploration, thereby, extending and enabling a tangible, ultra low power RRNS based architecture.
Sriseshan Srikanth, Paul G. Rabbat, Eric R. Hein, Bobin Deng, Thomas M. Conte, Erik DeBenedictis, Jeanine E. Cook, Michael P. Frank
HPCA3
2018 Extending Moore's Law via Computationally Error-Tolerant Computing
abstract
Dennard scaling has ended. Lowering the voltage supply ( V dd ) to sub-volt levels causes intermittent losses in signal integrity, rendering further scaling (down) no longer acceptable as a means to lower the power required by a processor core. However, it is possible to correct the occasional errors caused due to lower V dd in an efficient manner and effectively lower power. By deploying the right amount and kind of redundancy, we can strike a balance between overhead incurred in achieving reliability and energy savings realized by permitting lower V dd . One promising approach is the Redundant Residue Number System (RRNS) representation. Unlike other error correcting codes, RRNS has the important property of being closed under addition, subtraction and multiplication, thus enabling computational error correction at a fraction of an overhead compared to conventional approaches. We use the RRNS scheme to design a Computationally-Redundant, Energy-Efficient core, including the microarchitecture, Instruction Set Architecture (ISA) and RRNS centered algorithms. From the simulation results, this RRNS system can reduce the energy-delay-product by about 3× for multiplication intensive workloads and by about 2× in general, when compared to a non-error-correcting binary core.
Bobin Deng, Sriseshan Srikanth, Eric R. Hein, Thomas M. Conte, Erik DeBenedictis, Jeanine E. Cook, Michael P. Frank
ACM Trans. Archit. Code Optim.3
2015 Contech: Efficiently Generating Dynamic Task Graphs for Arbitrary Parallel Programs
abstract
Parallel programs can be characterized by task graphs encoding instructions, memory accesses, and the parallel work’s dependencies, while representing any threading library and architecture. This article presents Contech, a high performance framework for generating dynamic task graphs from arbitrary parallel programs, and a novel representation enabling programmers and compiler optimizations to understand and exploit program aspects. The Contech framework supports a variety of languages (including C, C++, and Fortran), parallelization libraries, and ISAs (including × 86 and ARM). Running natively for collection speed and minimizing program perturbation, the instrumentation shows 4 × improvement over a Pin-based implementation on PARSEC and NAS benchmarks.
Brian P. Railing, Eric R. Hein, Thomas M. Conte
ACM Trans. Archit. Code Optim.2
2013 High-speed formal verification of heterogeneous coherence hierarchies
abstract
As more heterogeneous architecture solutions continue to emerge, coherence solutions tailored for these architectures will become mandatory. Coherence hierarchies will likely continue to be prevalent in future large-scale shared memory architectures. However, past experience has shown that hierarchical coherence protocol design is a non-trivial problem, especially when considering the verification effort required to guarantee correctness. While some strategies do exist for verification of homogenous coherence hierarchies, support for reasonable verification of heterogeneous coherence hierarchies is currently unavailable. Ideally, hierarchical coherence protocols composed of `building block' protocols should be able to take advantage of incremental verification to side step the state-space explosion problem which hampers any large-scale verification effort. In this work, we prove this can be accomplished through the use of the Manager-Client Pairing (MCP) framework, which provides encapsulation and permission checking support that enables a form of state-space symmetry. When combined with an inductive proof, this ensures the validation properties of proper permission distribution and livelock/deadlock freedom are enforced by any hierarchical composition of MCP compliant protocols. Demonstration of this methodology through the MurPhi formal verifier shows several orders of magnitude improvement in verification cost compared to full hierarchy verification.
Jesse G. Beu, Jason A. Poovey, Eric R. Hein, Thomas M. Conte
HPCA3