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A Professor of Practice Path to Texas A&M University
Aakash Tyagi Professor of Practice Computer Science and Engineering Texas A&M University Accept the natural phenomenon Show what variables we are dealing with Definition of career What is Growth and typical profiles What does management mean? (here, pull up the variables chart and show what can be tweaked) Typical Career Growth chart Key Dilemmas
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2 Key Messages A couple of overarching themes of a career path and our role in shaping it My career path and the value of a PhD in Industry
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Overarching Themes for a “Career Nirvana”
3 Overarching Themes for a “Career Nirvana”
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4 Our lives and careers are governed by random events. On one hand, examples like Bob Simon and David Carr showcase great careers in progress suddenly brought to a halt by unfortunate events. Amy Pascal had a great career in progress but circumstances outside her control and influence led to some confidential information getting leaked and ultimately leaving her little choice but to resign. A start opposite example is that of Kirk Skaugen who at a very young age has managed his career to great heights at Intel and continues to influence and (in some cases) manage his environment to deliver value to his team and stakeholders. He is now the President of Data Center Group at Lenovo. Bottom-line: We must acknowledge the randomness in our environment but at the same time also recognize that there are ways in which we can nudge along certain variables in our favor to achieve our career goals.
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So it seems we could say that ….
5 DIRECTED RANDOM So it seems we could say that …. Our lives and careers are impacted by seemingly random events While some events truly are beyond our reach some events CAN be controlled and certainly many events CAN be influenced IDENTIFY, SELECT, and NUDGE variables and outcomes towards a desired direction
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Priorities & Variables in our Environment
6 Priorities & Variables in our Environment friends fun spiritual social At different stages in our lives, different things take precedence. These can occur in the most natural (accidental) way or can be consciously planned or “nudged” (Directed Random) exams Circa: 80’s Circa: 00’s Circa: ‘16 family awareness At different stages in our lives, different things take importance. These can occur in the most natural (accidental) way or can be consciously planned. service health teachers aspirations finances competition network
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Steve Jobs @ 2005 Stanford Commencement
7 I first heard this in 2013 but feel these words were always my guiding light Steve 2005 Stanford Commencement Connect the Dots…..Start a new path believing that the dots will connect down the road Find what you love. Love what you do. Keep looking, don’t settle Don’t waste living someone else’s life….Follow your heart and intuition; they already know what you truly want to become Steve Jobs Stanford Commencement Speech:
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And last but not least, a word on the Value of Risk Taking
8 And last but not least, a word on the Value of Risk Taking Simulated annealing (SA) is a probabilistic technique for approximating the global optimum of a given function. Specifically, it is a metaheuristic to approximate global optimization in a large search space. It is often used when the search space is discrete (e.g., all tours that visit a given set of cities). For problems where finding an approximate global optimum is more important than finding a precise local optimum in a fixed amount of time, simulated annealing may be preferable to alternatives such as gradient descent. The name and inspiration come from annealing in metallurgy, a technique involving heating and controlled cooling of a material to increase the size of its crystals and reduce their defects. Both are attributes of the material that depend on its thermodynamic free energy. Heating and cooling the material affects both the temperature and the thermodynamic free energy. The simulation of annealing as an approach that reduces a minimization of a function of large number of variables to the statistical mechanics of equilibration (annealing) of the mathematically equivalent artificial multiatomic system[Jargon] was first formulated by Armen G. Khachaturyan, Svetlana V. Semenovskaya, Boris K. Vainshtein in 1979[1] and by Armen G. Khachaturyan, Svetlana V. Semenovskaya, Boris K. Vainshtein in 1981.[2] These authors used computer simulation mimicking annealing and cooling of such a system to find its global minimum. This notion of slow cooling implemented in the Simulated Annealing algorithm is interpreted as a slow decrease in the probability of accepting worse solutions as the solution space is explored. Accepting worse solutions is a fundamental property of metaheuristics because it allows for a more extensive search for the global optimal solution. In general, the Simulated Annealing algorithms work as follows. At each time step, the algorithm randomly selects a solution close to the current one, measures its quality, and then decides to move to it or to stay with the current solution based on either one of two probabilities between which it chooses on the basis of the fact that the new solution is better or worse than the current one. During the search, the temperature is progressively decreased from an initial positive value to zero and affects the two probabilities: at each step, the probability of moving to a better new solution is either kept to 1 or is changed towards a positive value; instead, the probability of moving to a worse new solution is progressively changed towards zero
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My Career Path and the Value of a PhD in Industry
9 My Career Path and the Value of a PhD in Industry
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My Path So far… ‘83 ‘87 ‘06 ‘94 School College ‘14 Ph.D. CE No
10 My Path So far… ‘83 ‘87 ‘94 ‘06 School College ‘14 Engineer OR Doctor Good in Math and Science? B.S. ECEN Ph.D. CE Asst. Prof Intel TAMU Learn Practice Teach Practice No Interdisciplinary Research No more “prescribed” life flowcharts May the Lord help the child Larrabee Experiment in Risk Taking
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Value of a PhD in Industry
11 Gives you a choice to do research or development or both Gives you a choice to stay purely on tech path or pursue management Teaches you to never mistake the forest for the trees and trees for the forest
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Value of a PhD in Industry
12 Helps you build lasting skills in written and verbal communication Gives you an avenue to return to teaching when you had enough in industry
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In Closing Directed Random! Nudge your career
13 In Closing Directed Random! Nudge your career Re-calibrate with Steve Jobs’ famous words every now and then Take informed risks while facing mounting odds…Simulated Annealing works! Consider ‘interdisciplinary’ (and ‘antidisciplinary’) – it seems to be the growing currency of the future PhD gives you more tangible and non-tangible choices in industry career and beyond
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14 THANK YOU!
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My History of Intel Processors
15 Pentium® Pentium® II/III Pentium® 4 Knights Ferry Knights Corner Knights Landing This is how the technology principles just described are applied to our family of microprocessors. There are two major trends here: First, looking at the left-hand entry, you see as we move down, the chips get bigger. We are able to do this because of improvements to the cleanliness and quality of our manufacturing process, which reduces the number of defects per wafer. Second, as each generation of processors goes through several generations of process technology, so the chip gets smaller. A generation of technology typically reduces the feature size by a factor of about So, it reduces the area by about As the transistors get smaller, we can fit more on the wafer and the real estate is halved. And we have just seen what less real estate does to the cost! But, also as we move to the next process generation (or transistor size), the transistors get faster. We can use that additional speed in the smaller area either to increase performance (performance desktops), decrease power (mobile) or a bit of each. So, a given generation of products like the Pentium® processor will take advantage of several generations of process technology to lower cost, increase performance, or lower power for a variety of applications. 15
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Participated in these Key Trends
16 33.8PF 0.118PF Top500.org 14nm TBA 72 Knights Landing (Xeon Phi) Knights Corner (Xeon Phi) Knights Ferry (Xeon Phi SDV) Prescott (Pentium 4®) Tualatin (Pentium® III) Coppermine (Pentium® III) Deschutes (Pentium® II) P54CS (Pentium® ) ~8B, >9 22nm 1.23Ghz 62 ~5B, 9 45nm 1.1GHz 32 2.3B, 9 90nm 3.4GHz 1 169M, 7 130nm 1.0GHz 1 32M, 6 Human hair = 14 microns 14nm = 1000 times smaller than a human hair Petaflops Tera – trillion Peta = 1000 trillion = 1 quadrillion FPOPS 180nm 733MHz 1 28M, 6 250nm 450MHz 1 8M, 5 350nm 133MHz 1 3M, 3 Feature size Frequency # of Cores # trans, metal
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Democratizing High Performance Computing
17 Democratizing High Performance Computing 1 Kiloflop/s = 1000 FP OP/s 1 Megaflop/s = 1 Million FP OP/s = 1000 Kilo FP OP/s 1 Gigaflop/s = 1 Billion FP OP/s = 1000 Million FP OP/s 1 Teraflop/s = 1 Trillion FP OP/s = 1000 Billion FP OP/s 1 Petaflop/s = 1 Quadrillion FP OP/s = 1000 Trillion FP OP/s 1 Exaflop/s = 1000 Quadrillion FP OP/s ASCI RED was reported at $70M in today’s money. One can buy a Knights Corner Card on amazon.com for under $1500
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Growth paths of an Intel Engineer employed in Product Design
18 Growth paths of an Intel Engineer employed in Product Design Typical Progression of Roles in Design Engineering Progress Measurement Indicators Expectations Spectrum Grade Entry(E) E+1 E+2 E+3 E+4 E+5 E+6 E+7 Independent Execution Low Medium High Expert Scope - Technical Scope - Ambiguity Leadership NA Influence: Group-Wide Influence: Company Wide Typical Title and Roles in Technical Track ComponentOwner Component Owner Section Owner Tech Lead Principal Engineer Senior Principal Engineer Fellow Member of a Project Horizontal Activity Driver of a moderate scope Project Horizontal Activity Driver of a high scope Project Horizontal Activity Driver of multiple high scope project horizontal activities Solution Owner of Toughest Problems in the area of expertise at Group Level Creator/Driver of Technical Initiatives at Corporate Level Creator/Driver of Multiple Technical Initiatives at Corporate Level Typical Title and Roles in Management Track Component Owner Section Owner Section Manager Project Manager Director Vice President Member of a Project Horizontal Activity Driver of a moderate scope Project Horizontal Activity Driver of a high scope Project Horizontal Activity Manager of People and Cluster/Functional Teams Manager of Project or Multiple Cluster/Functional Teams Manager of Project & Organization Manager of Large Business & Engineering Organization
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