An apparatus and a method. The apparatus includes a plurality of long short term memory (LSTM) networks, wherein each of the plurality of LSTM networks is at a different network layer, wherein each of the plurality of LSTM networks is configured to determine a residual function, wherein each of the plurality of LSTM networks includes an output gate to control what is provided to a subsequent LSTM network, and wherein each of the plurality of LSTM networks includes at least one highway connection to compensate for the residual function of a previous LSTM network.
Please help me write a proper abstract based on the patent claims. CLAIM: 1. An apparatus, comprising: a plurality of long short term memory (LSTM) networks, wherein each of the plurality of LSTM networks is at a different network layer, wherein each of the plurality of LSTM networks is configured to determine a residual function, wherein each of the plurality of LSTM networks includes an output gate to control what is provided to a subsequent LSTM network, and wherein each of the plurality of LSTM networks includes at least one highway connection to compensate for the residual function of a previous LSTM network. 2. The apparatus of claim 1, wherein each LSTM network is further configured to: determine by a first function block ftL+1=sigm(WxfL+1xtL+1+WhfL+1ht−L+1+WcfL+1⊙ct−1L+1+bfL+1); determine by a second function block itL+1=sigm(WxiL+1xtL+1+WhiL+1ht−1L+1+WciL+1⊙ct−1L+1+biL+1); determine by a third function block jtL+1=tan h(WxcL+1xtL+1+WhcL+1ht−1L+1+bjL+1); determine by a fourth function block otL+1=sigm(WxoL+1xtL+1+WhoL+1ht−1L+1+WcoL+1ctL+1+boL+1); determine by a fifth function block ctL+1=ftL+1⊙ct−1L+1+itL+1⊙jtL+1; and determine by a sixth function block htL+1 as a function of ctL+1, otL+1, and one of xtL+1 or Σi=1NWh,iL+1htL+1−i, wherein xtL+1 is an input to the LSTM network, ht−1L+1 is an output of a previous time in the LSTM network, ct−1L+1 is a cell activation of the previous time in the LSTM network, WxfL+1, WhfL+1, WcfL+1, WxiL+1, WhiL+1, WciL+1, WxcL+1, WhcL+1, WxoL+1, WhoL+1, and WcoL+1 are weight matrices of the LSTM network, and bfL+1, biL+1, bjL+1, boL+1 are pre-determined bias values of the LSTM network. 3. The apparatus of claim 2, wherein ƒ1 and ƒ2 are each selected from a sigmoid function (sigm) and a hyperbolic tangent function (tan h). 4. The apparatus of claim 2, wherein the sixth function block is further configured to determine htL+1=otL+1⊙WprojL+1 tan h(ctL+1)+(1−otL+1)⊙WhL+1xtL+1. 5. The apparatus of claim 2, wherein the sixth function block is further configured to determine htL+1=otL+1⊙WprojL+1 tan h(ctL+1)+(1−otL+1)⊙(Σi=1NWh,iL+1ht,L+1−i). 6. The apparatus of claim 2, wherein the the sixth function block is further configured to determine htL+1=otL+1⊙(WprojL+1 tan h(ctL+1)+WhL+1xtL+1). 7. The apparatus of claim 2, wherein the the sixth function block is further configured to determine htL+1=otL+1⊙(WprojL+1 tan h(ctL+1)+Σi=1NWh,iL+1htL+1−i). 8. The apparatus of claim 2, wherein the the sixth function block is further configured to determine htL+1=WprojL+1(otL+1⊙tan h(ctL+1))+WhL+1xtL+1. 9. The apparatus of claim 2, wherein the Wh,iL+1 are identity matrices if an input dimension of the LSTM matches an output dimension of the LSTM. 10. A method, comprising: configuring a plurality of long short term memory (LSTM) networks, wherein each of the plurality of LSTM networks is at a different network layer; determining a residual function for each of the plurality of LSTM networks; controlling, by an output gate of each of the plurality of LSTM networks, what is provided to a subsequent LSTM network, and compensating, by at least one highway connection of each of the plurality of LSTM networks, for the residual function of a previous LSTM network. 11. The method of claim 10, further comprising: determining, by a first function block of each of the plurality of LSTM networks, ftL+1=ƒ1(WxfL+1xtL+1+WhfL+1ht−1L+1+bfL+1); determining, by a second function block in each of the plurality of LSTM networks, itL+1=ƒ2(WxiL+1xtL+1+WhiL+1ht−1L+1+biL+1); determining, by a third function block in each of the plurality of LSTM networks, jtL+1=ƒ1(WxcL+1xtL+1+WhcL+1ht−1L+1+bjL+1); determining, by a fourth function block in each of the plurality of LSTM networks, otL+1=tan h ƒ2(WxoL+1xtL+1+WhoL+1ht−1L+1+boL+1); determining, by a fifth function block in each of the plurality of LSTM networks, ctL+1=ftL+1⊙ct−1L+1+itL+1⊙jtL+1; and determining, by a sixth function block in each of the plurality of LSTM networks, htL+1 as a function of ctL+1, otL+1, and one of xtL+1 or Σi=1NWh,iL+1htL+1−i, wherein ƒ1 and ƒ2 are functions, xtL+1 is an input to one of the plurality of LSTM networks, ht−1L+1 is an output of a previous time in the one of the plurality of LSTM networks, ct−1L+1 is a cell activation of the previous time in the one of the plurality of LSTM networks, WxfL+1, WhfL+1, WxiL+1, WhiL+1, WxcL+1, WhcL+1, WxoL+1, and WhoL+1 are weight matrices of the one of the plurality of LSTM networks, and bfL+1, biL+1, bjL+1, boL+1 are pre-determined bias values of the one of the plurality of LSTM networks. 12. The method of claim 10, wherein ƒ1 and η2 are each selected from a sigmoid function (sigm) and a hyperbolic tangent function (tan h). 13. The method of claim 11, wherein the sixth function block is further configured to determine htL+1=otL+1⊙WprojL+1 tan h(ctL+1)+(1−otL+1)⊙WhL+1xtL+1. 14. The method of claim 11, wherein the sixth function block is further configured to determine htL+1=otL+1⊙WprojL+1 tan h(ctL+1)+(1−otL+1)⊙(Σi=1NWh,iL+htL+1−i). 15. The method of claim 11, wherein the the sixth function block is further configured to determine htL+1=otL+1⊙(WprojL+1 tan h(ctL+1)+WhL+1xtL+1). 16. The method of claim 11, wherein the the sixth function block is further configured to determine htL+1=otL+1⊙(WprojL+1 tan h(ctL+1)+Σi=1NWh,iL+1htL+1−i). 17. The method of claim 11, wherein the the sixth function block is further configured to determine htL+1=WprojL+1(otL+1⊙tan h(ctL+1))+WhL+1xtL+1. 18. The method of claim 10, wherein Wh,iL+1 are identity matrices if an input dimension of the LSTM matches an output dimension of the LSTM. 19. A method of manufacturing a residual long short time memory (LSTM) network, comprising: forming the residual LSTM network on a wafer or a package with at least one other residual LSTM network, wherein the residual network is configured to determine by a first function block ftL+1=sigm(WxfL+1xtL+1+WhfL+1ht−1L+1+WcfL+1⊙ct−1L+1+bfL+1); determine by a second function block itL+1=sigm(WxiL+1xtL+1+WhiL+1ht−1L+1+WciL+1⊙ct−1L+1+biL+1); determine by a third function block jtL+1=tan h(WxcL+1xtL+1+WhcL+1ht−1L+1+bjL+1); determine by a fourth function block otL+1=sigm(WxoL+1xtL+1+WhoL+1ht−1L+1+WcoL+1ctL+1+boL+1); determine by a fifth function block ctL+1=ftL+1⊙ct−1L+1+itL+1⊙jtL+1; and determine by a sixth function block htL+1 as a function of ctL+1, otL+1, and one of xtL+1 or Σi=1NWh,iL+1htL+1−i, wherein xtL+1 is an input to the LSTM network, ht−1L+1 is an output of a previous time in the LSTM network, ct−1L+1 is a cell activation of the previous time in the LSTM network, WxfL+1, WhfL+1, WcfL+1, WxiL+1, WhiL+1, WciL+1, WxcL+1, WhcL+1, WxoL+1, WhoL+1, and WcoL+1 are weight matrices of the LSTM network, and bfL+1, biL+1, bjL+1, boL+1 are pre-determined bias values of the LSTM network; and testing the hybrid recurrent network with the highway connection, wherein testing the hybrid recurrent network with the highway connection comprises testing the hybrid recurrent network with the highway connection and the at least one other hybrid recurrent network with the highway connection using one or more electrical to optical converters, one or more optical splitters that split an optical signal into two or more optical signals, and one or more optical to electrical converters. 20. A method of constructing an integrated circuit, comprising: generating a mask layout for a set of features for a layer of the integrated circuit, wherein the mask layout includes standard cell library macros for one or more circuit features that include a a residual long short time memory (LSTM) network on a wafer or a package with at least one other residual LSTM network, wherein the residual network is configured to determine by a first function block ftL+1=sigm(WxfL+1xtL+1+WhfL+1ht−1L+1+WcfL+1⊙ct−1L+1+bfL+1); determine by a second function block itL+1=sigm(WxiL+1xtL+1+WhiL+1ht−1L+1+WciL+1⊙ct−1L+1+biL+1); determine by a third function block jtL+1=tan h(WxcL+1xtL+1+WhcL+1ht−1L+1+bjL+1); determine by a fourth function block otL+1=sigm(WxoL+1xtL+1+WhoL+1ht−1L+1+WcoL+1ctL+1+boL+1); determine by a fifth function block ctL+1=ftL+1⊙ct−1L+1+itL+1⊙jtL+1; and determine by a sixth function block htL+1 as a function of ctL+1, otL+1, and one of xtL+1 or Σi=1NWh,iL+1htL+1−i, wherein xtL+1 is an input to the LSTM network, ht−1L+1 is an output of a previous time in the LSTM network, ct−1L+1 is a cell activation of the previous time in the LSTM network, WxfL+1, WhfL+1, WcfL+1, WxiL+1, WhiL+1, WciL+1, WxcL+1, WhcL+1, WxoL+1, WhoL+1, and WcoL+1 are weight matrices of the LSTM network, and bfL+1, biL+1, bjL+1, boL+1 are pre-determined bias values of the LSTM network; disregarding relative positions of the macros for compliance to layout design rales during the generation of the mask layout; checking the relative positions of the macros for compliance to layout design rules after generating the mask layout; upon detection of noncompliance with the layout design rules by any of the macros, modifying the mask layout by modifying each of the noncompliant macros to comply with the layout design rules; generating a mask according to the modified mask layout with the set of features for the layer of the integrated circuit; and manufacturing the integrated circuit layer according to the mask.