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Quantization ofWeights inaNeural Network
%252C+an+innovative+quantization-aware+training+method+that+could+fine-tune+the+per-weight+and+per-activation+precision+by+making+them+optimizable+through+gradient+descent.+This+approach+enables+ultra-low+latency+and+low+power+neural+networks+on+hardware+capable+of+performing+arithmetic+operations+with+an+arbitrary+number+of+bits%252C+such+as+FPGAs+and+ASICs.+We+demonstrate+that+HGQ+can+outperform+existing+methods+by+a+substantial+margin%252C+achieving+resource+reduction+by+up+to+a+factor+of+20+and+latency+improvement+by+a+factor+of+5+while+preserving+accuracy.&cdnurl=https%3a%2f%2fth.bing.com%2fth%2fid%2fR.dbe6f303894e583e9a646b4dc73f9573%3frik%3dUgEFzFZOoI73PQ%26pid%3dImgRaw%26r%3d0&exph=630&expw=1200&q=Quantization+of+Weights+in+a+Neural+Network&FORM=IRPRST&ck=E314A57F8C375540E73E9BA29981BCB1&selectedIndex=19&itb=0)
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