Hit papers significantly outperform the citation benchmark for their cohort. A paper qualifies
if it has ≥500 total citations, achieves ≥1.5× the top-1% citation threshold for papers in the
same subfield and year (this is the minimum needed to enter the top 1%, not the average
within it), or reaches the top citation threshold in at least one of its specific research
topics.
Chisel
2012584 citationsJohn Wawrzynek, Krste Asanović et al.profile →
Garp: a MIPS processor with a reconfigurable coprocessor
Countries citing papers authored by John Wawrzynek
Since
Specialization
Citations
This map shows the geographic impact of John Wawrzynek's research. It shows the number of citations coming from papers published by authors working in each country. You can also color the map by specialization and compare the number of citations received by John Wawrzynek with the expected number of citations based on a country's size and research output (numbers larger than one mean the country cites John Wawrzynek more than expected).
This network shows the impact of papers produced by John Wawrzynek. Nodes represent research fields, and links connect fields that are likely to share authors. Colored nodes show fields that tend to cite the papers produced by John Wawrzynek. The network helps show where John Wawrzynek may publish in the future.
Co-authorship network of co-authors of John Wawrzynek
This figure shows the co-authorship network connecting the top 25 collaborators of John Wawrzynek.
A scholar is included among the top collaborators of John Wawrzynek based on the total number of
citations received by their joint publications. Widths of edges
represent the number of papers authors have co-authored together.
Node borders
signify the number of papers an author published with John Wawrzynek. John Wawrzynek is excluded from
the visualization to improve readability, since they are connected to all nodes in the network.
Haj-Ali, Ameer, Qijing Huang, William S. Moses, et al.. (2020). AutoPhase: Juggling HLS Phase Orderings in Random Forests with Deep Reinforcement Learning. arXiv (Cornell University). 2. 70–81.5 indexed citations
Zhang, Ben, John Kolb, Douglas S. Chan, et al.. (2015). The Cloud is Not Enough: Saving IoT from the Cloud.. eScholarship (California Digital Library). 21–21.107 indexed citations
Asanović, Krste, Rastislav Bodík, James Demmel, et al.. (2009). A view of the parallel computing landscape. Communications of the ACM. 52(10). 56–67.369 indexed citations breakdown →
Yeh, Joseph, et al.. (2000). Stream Computations Organized for Reconfigurable Execution (SCORE) Extended Abstract. 605–614.23 indexed citations
13.
Perissakis, Stylianos & John Wawrzynek. (2000). Balancing computation and memory in high capacity reconfigurable arrays.1 indexed citations
14.
Asanović, Krste, et al.. (1996). T0: A Single-Chip Vector Microprocessor with Reconfigurable Pipelines. European Solid-State Circuits Conference. 344–347.11 indexed citations
15.
Wawrzynek, John. (1996). Spert-II : A Vector Micro Processore System, Special Issue of Neural Computing in. Computer. 29(3). 79–86.6 indexed citations
16.
Lazzaro, John, John Wawrzynek, & Richard P. Lippmann. (1996). A Micropower Analog VLSI HMM State Decoder for Wordspotting. Neural Information Processing Systems. 9. 727–733.7 indexed citations
17.
Wawrzynek, John, Krste Asanović, & N. Morgan. (1993). The design of a neuro-microprocessor. IEEE Transactions on Neural Networks. 4(3). 394–399.30 indexed citations
18.
Suaya, Roberto, et al.. (1991). A topological framework for compaction and routing. 211–228.1 indexed citations
19.
Wawrzynek, John, et al.. (1990). A Multimedia Digital Signal Processing Tutoring System. The Journal of the Abraham Lincoln Association. 1990.1 indexed citations
20.
Wawrzynek, John. (1989). VLSI models for sound synthesis. MIT Press eBooks. 113–148.12 indexed citations
Rankless uses publication and citation data sourced from OpenAlex, an open and comprehensive
bibliographic database. While OpenAlex provides broad and valuable coverage of the global
research landscape, it—like all bibliographic datasets—has inherent limitations. These include
incomplete records, variations in author disambiguation, differences in journal indexing, and
delays in data updates. As a result, some metrics and network relationships displayed in
Rankless may not fully capture the entirety of a scholar's output or impact.