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+algorithm+converges+inexactly+when+applied+to+model+training+on+non-ideal+devices.+This+paper+puts+forth+a+theoretical+foundation+for+gradient-based+training+on+analog+devices.+We+begin+by+characterizing+the+non-convergent+issue+of+SGD%252C+which+is+caused+by+the+asymmetric+updates+on+the+analog+devices.+We+then+provide+a+lower+bound+of+the+asymptotic+error+to+show+that+there+is+a+fundamental+performance+limit+of+SGD-based+analog+training+rather+than+an+artifact+of+our+analysis.+To+address+this+issue%252C+we+study+a+heuristic+analog+algorithm+called+Tiki-Taka+that+has+recently+exhibited+superior+empirical+performance+compared+to+SGD+and+rigorously+show+its+ability+to+exactly+converge+to+a+critical+point+and+hence+eliminates+the+asymptotic+error.+The+simulations+verify+the+correctness+of+the+analyses.&cdnurl=https%3a%2f%2fth.bing.com%2fth%2fid%2fR.3b9a2859df075371ecca9599f7b1bec4%3frik%3dBc3z%252fNPQoG97zA%26pid%3dImgRaw%26r%3d0&exph=630&expw=1200&q=Gradient+Placement+A%26E+Tutorial&FORM=IRPRST&ck=3A7EA9B71EF49226E4D27CD9835D4169&selectedIndex=17&itb=0)
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