katie pfleger julia sheth alana anderson nicholas sparks
play

Katie Pfleger Julia Sheth Alana Anderson Nicholas Sparks Pearce - PowerPoint PPT Presentation

Katie Pfleger Julia Sheth Alana Anderson Nicholas Sparks Pearce Kiesser Co-Manager Co-Manager Language Guru Tester System Architect kjp2157 jns2157 afa2132 ns3284 pck2119 MOTIVATION Machine learning is now more prevalent than ever,


  1. Katie Pfleger Julia Sheth Alana Anderson Nicholas Sparks Pearce Kiesser Co-Manager Co-Manager Language Guru Tester System Architect kjp2157 jns2157 afa2132 ns3284 pck2119

  2. MOTIVATION Machine learning is now more prevalent than ever, with 51% of enterprises deploying ML Matrix manipulations are often essential to machine learning algorithms

  3. PROJECT WORKFLOW: TOOLS

  4. PROJECT WORKFLOW: TIMELINE nov. 27 th adding dec. 17 th matrix to present! codegen oct. 15 th dec. 15 th nov. 17 th LRM, scanner, matrix C library parser operations working sept. 19 th nov. 14 th dec. 14 th project proposal hello world print matrix

  5. ARCHITECTURE:

  6. LANGUAGE OVERVIEW: comments /* this is a comment in MATRX */ function declarations: int main() { return 0; } matrix foo(matrix m) { return m; } non-matrix types int, bool, void, float, string, char control flow for (i = 0; i < 5; i = i + 1) {} operators while (i > 5) {} +, -, *, /, %, >, <, if(i == true) {} else {} >=, <=, ==

  7. LANGUAGE OVERVIEW: MATRICES matrix declaration: matrix m; /* declares a matrix m */ matrix initialization: matrix m = [[1,2][3,4]]; /* declares and initializes m */ matrix n; /* declares a matrix n */ n = [[5,6][7,8]]; /* initializes n */

  8. LANGUAGE OVERVIEW: MATRICES matrix arithmetic operations: matrix m = [[1,2][3,4]]; matrix n = [[5,6][7,8]]; printm(transpose(m)); /* prints the matrix m transposed */ printm(matmult(m,n)); /* multiplies matrix m by matrix n */ printm(matadd(m,n)); /* adds matrices m and n */ printm(dot(m,n)); /* takes the dot product of m and n */ printm(det(m,2)); /* takes the determinant of m, which has dimensions 2 x 2 */

  9. IMPLEMENTATION: MATRICES We used a C library to implement matrix functions and then linked the library through codegen. struct matrix { int num_rows; int num_cols; int** matrixAddr; int buildPosition; };

  10. TESTING: ● At any given point, each new feature in codegen is semantically checked ● Used regression test suite with target pass/fail test cases, ensure that other features still worked ● If necessary, perform manual checks

  11. DEMO

  12. THANK YOU! SPECIAL THANKS TO OUR TA DEAN DENG!

Download Presentation
Download Policy: The content available on the website is offered to you 'AS IS' for your personal information and use only. It cannot be commercialized, licensed, or distributed on other websites without prior consent from the author. To download a presentation, simply click this link. If you encounter any difficulties during the download process, it's possible that the publisher has removed the file from their server.

Recommend


More recommend