# Avoid low depth Activations

- [Hardware Characteristics of Hexagon Tensor Processor](https://docs.qualcomm.com/doc/80-63442-10/topic/htp_guidelines_low_depth_activations.html#hardware-characteristics-of-hexagon-tensor-processor)
- [Network design techniques and examples](https://docs.qualcomm.com/doc/80-63442-10/topic/htp_guidelines_low_depth_activations.html#network-design-techniques-and-examples)

## [Hardware Characteristics of Hexagon Tensor Processor](https://docs.qualcomm.com/doc/80-63442-10/topic/htp_guidelines_low_depth_activations.html#id1)

The matrix processing engine of Hexagon is designed to be very efficient at dense dot products
down many channels and producing many channels of output simultaneously. Activation data is typically
stored with a fixed granularity of channels. In other words the channels are typically rounded to the
next multiple of 32. When the number of channels is much smaller than 32 (like 4, 8, 16 channels) we
currently have low efficiency and performance can be sub-optimal. Additionally, if the number of channels
is not a multiple of 32 it can cause inefficient memory usage due to the amount of padding that needs
to be added for the fixed sizes.
Consider network design approaches and/ or architechtures that maximize the usage of channels.

## [Network design techniques and examples](https://docs.qualcomm.com/doc/80-63442-10/topic/htp_guidelines_low_depth_activations.html#id2)

1. Use convolutions/matrix multiplications instead of separated out discrete kernels wherever
possible to take advantage of the special purpose hardware.

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> - Example: Figure 1 below shows a sequence in a network
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![../../_static/resources/htp_guidelines_fig_1.png](data:image/png;base64,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)
> 
> 
> **Figure 1**
> 
> - The sequence starts with an input of shape 1x1280x720x3.
> - After this we slice out each of the three channels and work on it.
> - This is very inefficient for HTP as the number of channels is too small (1 in this case).
> - After the slice, lot of what done is elementwise multiply by a scalar and adding it to other values.
> This is just convolution! If we fuse this sequence of operations into convolutions as much as possible,
> as shown in Fig. 2, our performance on the end of the graph would be much better.
> 
> 
> 
> ![../../_static/resources/htp_guidelines_fig_2.png](data:image/png;base64,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)
> 
> 
> **Figure 2**
> 
> - Further still, we should combine the 3 convolutions on each of the 3 channels into a single convolution
> kernal with input 1x1280x720x3 and output 1x1280x720x3 as shown in Figure 3 below.
> 
> 
> 
> ![../../_static/resources/htp_guidelines_fig_3.png](data:image/png;base64,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)
> 
> 
> **Figure 3**

2. Do transformations to the graph in such a way that the transformed graph is functionally
equivalent, but where feasible, kernels are coalesced to increase the number of channels (optimal
number of channels to have is 32). This will improve efficiency and performance.

> 
> 
> - Example: Consider the Figure 4
> 
> 
> 
> ![../../_static/resources/htp_guidelines_fig_4.png](data:image/png;base64,UklGRv4lAABXRUJQVlA4TPIlAAAvmEOIAP/iOpJsVdnnjvPpWWg8xO4O1/ek4TaybbW5YmYpNUaqzJ163AU7FDNet7FtK9XG3SWlEFqibFII4cuV+Q+AQgBOteI/JaLlRfxFyo8uc0aofRp8GrxrvGu8KrwqvCo8CtxzPEvcMjwKnBjKCgMMRuFoGAzG0WTg98KxMm0yw2DQ9zp9r9P3Oj231XNbndPonAabiW6FaoXqlBNuvmnpTqlWhM2F2YQdgFOtAqF7AUK1gjuEzo6GmxM4OxhS2fXhty+/ffn9h1TcBalIxV0IhABgoDQTEQAh0AsK63SVKZHZdTJMDpki1unpOHw10iI6XC53of7m9zW/t/k9zeG2g6I2j41VFLdt45j7j329viNiAnJr009a2fpczT6iPEAzieack50jiR6GTBOhx4Tbqk7Q7NLsCsZuSWwDHZBHQlDTu0NUa+lZn7NFBPb7cs0T9kc4QZKQN6ue4Nh8KOvatrdtI2U3yVx1e69/vZMyvffee+97/qchAiBFSfhIIGOGpB29RBRDsAIaFkQqov+yIMmO26ZfooV8FYwQpHwwhH59O6Bbt7ZtW/2t/0Lt93v/pzH//0siklJqR0MHWubFSOSpwsBAJBiUbAQiIvrvW5IkS5Ik2yJNq76Ze5KZmoioilv/wK/1ZVX+q/xX+U8WrulLL1X+u8Ha3z9882ecb3/Br1TAvwd+/Ivwewe+88EV+h8yC9iYQWsKWmcOsgKiy+2D/QMJLB2Mjp99QpcLjureMS8cqH4oNjmFOBktcGB6A9HaoYdXtMiVUuvj1oEjH3F27b9xwmcz2gXUQTLEYgV9mC1QSxgHNrMczl9RL9SQS0bFIl+q08o+bhz01TzO0cYJl0vrDBE8acSfdoAsjxv2hKW3qqAAXIxpPd6SyvLEeeNgGIEHfNFJXFIwpUVXQyyPwAo3VVK+dVBWeATHbuyaMk6aTxj15vEP41jGajbJYjfKgYOUW92WHk5VCG4rchBfhSgvUhbpZpnF/4UFddiJbJQN024mDNWxwS2BoTTMzwpoMABnkpVwEWJ7RzPOxGY2GbRqu1UdFxb3iTluJ9iOrNVSyNRhHJgVfRHJZkXrREnZr66YJqK0s4yNOgHI5w0Fm8ApEl3s9hDNq2qRcGItKC7oxhVUh8wkntKyuayK84ZCB9YENvQh9pM2NOEUwgFkRsMmHYuZdAphB+owKc/k2wmOVXUIyIfUxz0xrfEvWoJHm8y7QOVD/Fs3rGZDq8MFTsybCQumbcgCscOcxvaKg/1ss6CSn21GYQXY4dHDBnSYjYpCO8MVF6HOms/HjYPNfaK5D+shaid2s9O9YbovVoDD3YmCortnKLHrfihO9oaR42BNY0pFJrK7maMeXuGsU3yiX0B376jjxsEEHQvdqRZVM2cuqy0QFKy0CtQWgn54BfIivIbUscjfrEw2y6/Z09X5gdXGQd/Cv2H+383+z3+qGFyvxdXrRBfFnr+sLaP3WtFOW6b3vTg1Y4hTU0bvtaOdKPVj8Kkhg08dGWxqyWBTT4a+yXs+++Tj9+e63nn57TDXtdku14ra1Q/D3NdbYe7rIyhpn4bw/y++/JXMK/rjEG6q6HZ493Mr/zq5HdRzc30rTP5S8Ces1qrZMhxbGuo4LFWzbfiCCD5sVbNNiESIYaOahWCpqBAq/x0G48IfNaP7/vA34M+/Tfxpomm0oUsNtr9bLZca0i2sBHeWevFOjQd0pRXqFMsm8eAJcBK4r3ybbavlHDVYnoqB2Uv8ZbXysPb+pvn9qsaJnnu323/BpJGihU+Ozb9Jjtu3ma9/95ru56f7SWMltue/EdXi5HRSt7DRdr/YcAs4FVrhlHUKrmp99xtHG6wymtwwdaTvrFLc4u8cYanxPlBzmlim3DkSvIkH060Hj3ScE5wkP5HZpnygTfKBTsC8Ypv04LaO065OgBPmxzCoBb/YMNlFYJl05nbdoKlPNeZ5oJX/bqSnMa8ypjGvy6kxr2StLdd+cGutL1dL8gua9QV5j4+C/Ov/oWnWsBdefXP+17Cnb5661KMH2vI5hT11RX369pdvPIHfPn6g/D6B7/8gnD8C2v7mCt95BAUbMdnSFNpGlDJx6g9/dSQwMkA5ML0BW0gC0MMzdJjtQaGiRVKIkwFEZIui+UQfEXbFDYRd+xqnVN0ZeQ+xjnphYiEZYjWrelEYFUvKZaBFZBv5aMYq48jHDYSjjTPO3othioUbIGtkGvFuOw6yusaRwMQiyw50kmHQZfpqfgNhmDFURwhWHcElicyK7mKOnSy+MS2TwCZ1l6vInCxnKGyFs3xiw24imMCKbOZksRtlJ4V2UXghp2YWzRcW1GEnslEatogsX51P0BX30DrM7NaCTZwF01KY2FgLF+TzkwMzh1vVwatzjcm6dEl2YlrDaf9SlInzipb62dgDRwth60D1kdpSX8DG/JyW+vkaZVnROlFSWJnDGrx0F1Ez65DKs8NlWZnF7EBDs38pdgXOK1rqPdw4QMMfR3XvmJbYUh9ORsP8lJb6CJRm5oBbAovqCBxopDrsEpNYS4qKIcpGCI+7Qse/CNN/e7usuw0ved8jEvqog2SI5bqW+oHtM1rqE+NQnh1ATmBRXcR+UkEVTmEFB3VQNrSIrHwKLThY/kVIaW9f21LfMYXJO3Be1VIfqvYJLfWz7tzPwFlxzeK/tMrX6cMr0Ddhzd93mRVsuLQ7kN52SXpFpvg50SZOgVho287sAP4Qb1jNhlaHUyqWqGzhz3Kmu13MfymGBRJa6imY0vKsdlVLfYV/Ukt9BhzAFYv/onp4pev0ielHhZO7Uf1QbCKLkzHh4u5AetslYR9R/Iyqo6AOTiy07aSxvaDbgA6y+itGHuYKVpZn9DDrONkvPORhblj/nTDCtS31g4T1Bt2Wrst1LfWK8jkt9RmY8IkrFv9nps/SZuwWDCjJ6MNsgVrC6Cji7iB1S7Bcd5vC5iQ9DveF7qgch1lxd1T33awiuztR6KiHN8VFwYMCyeQ7mpArJs3nEdkT2nxikpYWWR0Ws819oo3jGTWubKkPRWGrWGi5rqW+o31GSz0/CYvuqxf/B1zIQB1mB11PNOwJDyyyvAhJ3RJs+qUvbBx0rGzb1bHYyVyymXIFto3RfEobHmHJeGCVvlmUlTY8vDLtQWYyr5vUuLKlntly6MCs6MsVLXUi9Akt9dmwYQz6iBWdZsOSWGh5po/IoDfABVnhRp4i8pYgYR+h9E43E8PKAbcENqbQyWZCtVzRUl/Q7fqW+izETDNWll8sSidh3dDPXcOpCr7LislaQZV3B/KWQN5HKEUHkBPY0HnFY23zK1rqDdsntNRnb6iODW4JnCzMVwUmKbOcGZO1A1tsdzA5EPYR6lBaS10+pI7viZneUi9Q+4SW+uzZjqzVEhiVOpSJrMhDJEdkSXaQdwfyloDvIxSitJZ67DDHQb/2SG+pO/xTWuqzZxM4U2hw9ovNYuNAF/GY7IpD3h3ITL6PUIDE9nZiS70Ah4exWFF09wwtyS31oSif1VIfx6TT4/GsdGBNgLezMjVp2MRTmKz8U5Kc7WJbggPIKpDY3k5rqUuvxK/0tR7UVtJb6jvap7XUuW51M+JYVYdA7LAOJuTW+BctEdkNOXqIW4KEfYQW+U/AJoyX0w05hvzzzx7I2Nl7UgtqTHbijB7ilkDYR+gS8qK7o5Cwlxf/G9TJGLYD2b2jk9DvfihOSVZsGou7A3FLkPaKjNYgLbrZTrWoyu+YrBAU7MwKdB/8Pfq8mCQrfrO4OxC3BGmvyCiWO9ryP5p+9vGGtiqjniaynnvhxSevk1b1rowewu6lsepH2juetHq+lCJ0dCv/Vf47TMO1WlwPl0PNE0zVI1MNWfqUCdMT5OIzQnfuLYd2KVPlgMbRff4ySVE+0/xO6DHvSYzee5IiJctnHFA2utSl91acpTHESVmIk6gQZvmMA9pGV5xEhShLZIiSthAlWSHI8hkH1I2uKMkKPktl8Eld8ElYcFk+44C+0eWTsGCzZAab9AWbpAWT5TMOKBxdNkmLJMtnOIk8ewWZ12a7XJM3rFJB5ER5p35TClvS1Q+C/OsjkDeshJLGRHlDClvSpyH85+evv5F5daMJ4SZ5w0oy8rckhPd+svKv4N4HdcNKc5P4LWm9DxQMr7Xh/dWaumGl+R1o35KWwVgayoQlecNKMtq3pG0YiTCGLXnDSjLat6RN6IjQhQ15w0oy2rekECwVFQJ5w0opOidK5b9qBX/9cT64C/l3NErAVwDwQw4A4Ht2bJP6Lsu/HL6VFABwsx/WJ4BYrIEpM+8ewBSCr5WOlhkTN/2fNiRdbPmh6oEMYyspAzNWxs16WGcLz0ws1tiJMBsGec45NGYQYWSjR5vrw5koGKoGLrX7g5w0MLMb1qTHpy+hnrizn2YrHwbCQeCLzfdzArhzKyHUnYht0diUkpQWzRTDyveqx5DLsFo7FHqOeyDYwgJ4SDX2yfY7A5oIGO4rQrQtGr73SRcMuEeTDf/4nD1ceFi212yfB3aTk4Lo0g5U5D6/cVFo4LHyfAuSIj0GKXJxumFlN4cWOQwrzxHFXYzcdt0C93Dek6zBhOBIYOAedj7xYrHdaYpvL8LE/XVvL2r8BIWWPGesfOAZe94nOoudTgyeL3Ye06dBpEErRdFNOayO2QW4XIaV2ZkU7zd5MwY03PbRon/xc9/s4bEmCjYax2K+j/m+CONBeYQYF+obHoSOgem+1LS3EAjv9mJ35wFy14WIkLGphpXbeWPIZ1itQVPc38REdiLzu3WhID/YSpjf6k1MCDKtt/nquFM63vRKnvxzD9z5dckNjZ0YHV3IcBFWqMcgSVMNK/t2A3Ia1haDLe4cAABZPEFGFqz6vh7P10Nw6fS/Zq6MB5oGrYy091WljCa094N6oRFr0UiRwbTD6px1Jp9hHdDawk40OTKhT30mr/ejt1d/v9pDfP6A+K7xjraFlZGJGd/8RXi87zQ0TMoHZ4ZVinq0Uw7rAIs+l2HtYWyR1yKziXsKD8qzPS7uuKMLUNHWIlfhEA3zifKfkJOE15MExjPcu0b38xHuaI2vIIUaRCtHtmW/4esyD6uFi7kMawNnCz3L/LObNh3rH7ZGRDQBOtoMj9H7xWiddY+J9rszBqBnsd0ykIXXBICn9DpcNGIaCxy22sy/K9eDKSchtmfGY4phNWhzGFbB11CxSGPPMe9ORGabyeLVSNCIcH2ZRP0eRJ8U8WfTOwB04SpiFO2BItAOLI15VsuNKQy4ikRpwZfGvKn896WjMa8ypjGvy6kxr2RN5aX092vyhpVSdE4UfblaEn27viDvsQryrz1IHFYym2ROFEksCe418tawtyuka9gjb0sqyk8EqVYxT12q8l/lP7r9/cM3f8b59hf8SgX8e+DHvwi/d+A7H1yh/yGzgI0ZtKagdeYgG73S//4dnTuTut90Xkiev9IqtA1+h5GJzvAK5CViDzZckpVmNdWwTNsVCSwdjI7mE7pccFT3jnnhQPVDsckpxMlogQPTG4jWDj28okUiR5lPpj+Tt5GBQOr8FdXDK7mtQ9G8KfHpZDJU/rM4GRMuyYqzmmSYblF3fWcFE8iK6kLi11aS830GG6cgKybJTEejIsCRjzi79pWzkOlsF1AHea4XK+TBXaCWMA5sZnknxaVeqCECRsUSeTpWzPsdS1upuhNS5+/Mg9zqLcisJClmsw2NTEa2hNFRRNnrZrXHMs2C7vocoOwkc5EbekIPz9CRmu93J6PilGTFJJl+NCoG+moe52jjlOeEPP4LJo340w6Q9V3DnvAAqQoKQDA+gsOGQwSV+XfHIG17L0a4bv5ItcnEZATJDhIgFSPOiSzLirOaZphmMXd9Ez4p7nTvVILMQu7batauyPcDVZRNTpKbrj8uBoYFhAAtOolLCqa06KrZxAiscFMdF7oOygqP4NhZ8LPgOHDyYEInbcPsgnSb9/C5YUms7IlfzdAtkHGKE8xVRK6b1SOcp1rIXd/Q0y5Io4e7oTA2A4Pe4YKegGMVZZOTZHBn58WACQ9yxsozbpQDx4UNui09nKoQ3FZkeaIripRFuvQIX1hQB6016Fwfpc0YbG8wlOwtonTsZqMjF0eT1iWOMzbBVlBl2cRZFfVw4e6bUcR0bsMSkLZFi51sVaaQ8iVDdYgkJ8kz7wuHoTo2uCUwFIX1GqDBAJxpNnEWTCEJNONMbGaTQau2W9VhRne6Z9In3PqG0xI4UVn/BCRATgGPTbAd2ETZxFkVDReuOGI6N4sxgHHxUnghp65w5hbNl7ahybLpSbKAsB1Zq6WQ4ezhnhV9Eckhu9YplOeJk7NimojSzjI2otSF0XUggR1oaJbAqNThrJoBl1kjE0yygyybNquS4eIS0nkKTu97mQD2IChwxvKldRRRNjVJFhQ2gTOFjc1bJ3lYtUg4sRYUF2k/s6gOmUk8MaXfB8d+TYKh0JFCg7NtxAihcoicsQleccRkU2ZVNFxcQjpPYGH3fdU6obvMEsuXJ6Ysm5gkC4sOrAls6LwJwtdBE6LJAWRGwyYdi5l0CmEHqlhrcESCosCSQEOTN3MVRdzHxSa4osRkr5jVNZYLjKfzBEanN/sgpbdiF4nmy4w9IpuUJAsLx6o6BOSDNRzYLDb+Q8ISPNpk3gUqH+LfumE1G1odLrDCigQTG6ZELLQHJpM7ecuzamyCN+TokTSrguEC4+k8zuj06VmoXUEVT4nly4IalU1JkkXFgmkbskDsMMfBF3I7Ju1DkJ9tRmFPisOjhw2SqEdFIfNOvo27rT0FVrg1rDGkVSPt0Y3wvx1w4tliEzxxRo/0WR1wvsh4OheIhvbOlAAr/ItyLF82rFHZpCRZKGzuE8190J7AxG52ujdM98UKcLg7UVB09wwldt0Pxck6lI5DbBqbVWQiS3Y69fAKZ53iE91sTqb/N+WtuDtquK1FQ9SqFnn+NqiTMeyANj8qDjIr2Rt0mDjBdrLyJcqmzSrXw0UmpHNONLRPdKG50XESzy2SL4fqiMomJclCYYKOhe6liqqZM5fVFggKVloFaivSu5XC0qljkb9ZmWyWXwSnD8SBlais3ZmfQ/97iKk6MnUwv05+W9nib0cW589WCAq2BZ25Rd5cFSZY/OVFlE2c1ewfeXF2fWI6N3evcCfvFnV3Jwod9fCmpJxAm09Mk/OlOZpdEGXlJJntaETznX+tcbcWV/SE1gLs+sR0bsxFzbKkMLwDms/FJzIJ95ck4gOirJwk06oRqa48wVQ9MtV4guoZofOLKvU80Mu1uO5QF9GrVP5hoce8JymEqVZ1aUlbRK9W+YfFSVQIUrXqxElbRK9a+YdFSVZwqV51oqQtolev/MN8EhZMqlgdn7RF9CqWf5hN0mKSalbHJm0RfQlFZTjq1tbxyjtzv1qSQo+2GfN2/WZY5BlzdR/mv47CvNdHKLMWfMa8ERZhxpC3Iq8yi8AZQ94a9sos+mYMdWuEXK3LLPpmDHnrpC21yJsx5K0sutQib8aQtxb3Uou8GUPes1coo+h8WhmV/w4JcwHAjzn1rk04JPVdFn/FD3rJwExz46bWprUt2kScvN23QJwW6s1XAGBmfyhzAKKMNPnO4hgZbH9b/AIG5aZl5rYzsz6UGcNMIglp0aTssxoJ/OcEcOdWHZOJsTJ0+EVrvWrT8fgHS1l6GUhIe8duGrlbb1wUGsLNe5DY52ZOpMeQ9s/TKzdPvK4oZdxqoieWMil6t1jNnugsNfRg0EgKaJQBg0iDNqX/xio373ZNKeP16vm+upQJJpTUtDeLcyZzxwy36h4EKHA2uwFWvcHzylL2Dlrv52eUMgNM/soNNZCh500wRVibfX+HQcm5spQ9yOk9tZRl+bayhDxx5W19tcb7bQ6NWItGpAdXg1pzVSmj/9vjnVzKMn0DSz46HvGFSISJFzrNHL0VyHRYVnA37X1VKaOnvR/JpSzTlJHQLzY8aMO30XXGHc9XhBfam/9YJdQgWuVnYl5Zyh7vO+6fVsoamdxEl79NejUjypOqgI5W9KuA6s/rSRu+z+RSdkdrn1LKYs88xtZMvzw/AeB5RSlrmImlLNvx8IaZ081a8PULkvNADbiKGvME7UpVGvMqYxrzupwa80rWxF1Bf78utcibMfpytSTadX1B3mMV5F97+FKL8BmzXm4380/prGGv0M6Y88u+hK76sjgOb+W/yn+V/5S8P7/7JoG/f8BJBX6u+GiDKijw4xkYv1Yg/yWzgg2PyNrv3wP9jzirzq88lIkzzvgFTgW8C1GxdaD6SI2ABWzMoDUFrTMH2aT4XDAOJLB/gFJUD6/ow2womjclPh3TG1T+szgZEy7L2oHqh2KLTV0GvvKwKxJYOhgdzSeNCkd175ipETCcjBY4SCgRrR0kblpCfC6YCZ9xHPmgnDl8tmILMiu5WdlsIzdrRbaE0VFk2UI+uuAjkmar7l95cOQjzq595YTPZrQLqIMEwyJGQIY6QoTknYRSvVB1IXGzxONzsYyPPy2BvprLN5ysTTIxGSGSO8iapGLEOZEjsgfIoqRhl+euF/vKQ1/N4xxtnBCXvaTkLJg0BE4xArJMPRUUgGB8BIcNRzQ+F4wNY9A8t8ODSxIrK8+rGboFMk56x8vHZGScESYGvyZVkjR1hMv62sMwhvDYE5cUTGnR1dQIoKzwCI49sGCyMCKrguEXjlHYmmF8kDVHlB5u0ejIxdFI/hPcMtYgvlAKakxWYYHzworM1+FfexDjQn7soxw4SLnVbem6XBUBVlGkLNKlonZhQR189bnALhc+reG0BE5U1m0DucungIf1x9jg0i4hJgvpkmziLJj2dQnhsU9gKApr4ULLdRGwo4kRuJlNDmhoVR1fOnagoVkCo7IGVP3IgMuspAulVZgFHaKsTDYrWifKVyb4zT6LDGdFbFb05aoImDg5K6aJKO0sY7MvnvEBHSk0OFteDCsTh8hJrAXFFYcsK0IsHHD7ygS32Wexsee6h2heVcsVEbCgG1dQHTKTeGLal0/5AJYEePVmVbiiCKcQOuRdQkRW2uoTDiB/haIHjjPY0MXn3Ta0KyKgYZOOxUw6hbADdXz5TGyYErHQHphM7uR/a/2gH+IL5w1ZPsSueMMS5qtNnF+XEGz2ArHD+FsAmOkRUKDyIf6tG1azodXhXzwr3BrWGNJul+7dR/jfDnjwZJ1hIRFMMYlEmuULKmmWj4I6vioh2OwFYoc5Dra1S48Ah0cPG9BBAqzQxWjF8mXi7hXua9iJk7ZRMdvcJ5r7sA3qZAw7oM2PioNsgrM3ct96uG0Tu9Q0NlnWJrofipN0itkMFXdHdd+/1iDGhXCzP3dvmO6LFeBwd6Kg6O4ZWtIigDWNKRWZyO5BqR5e4axTfKKL8blAQMcH26nuyGaTySy2QlCwLejMjb9Hr23wrUJRFb9ZlBVesxd+dO1Y7GRa+WsNYlwIN/uRuay2QFCw0ipQW7HECJC/WXkwyK/Z09X5gVWKT415ulnlv8p/h6UiYSlgm+1yrc5d3QfJ1kdQ0EhYbmY3mhBuqmdULNA2uPehnJGxpOnw/mqtmtGxCHgTlqoZHU+bYQxb1YyOJ5rShY1qRshTMwqh8t9hGu7oS/UTd/P3Y5wPP1xoPosH7p/Mxes7RayeNHLyRlJu7j0APP9Rdv3zdaeKMY18tBnePHD/3DTR/vP5iFu1OsY1ZIGd8fkIWyqZoJE3A2BemLi/gedrApExcA+Cv0VvQJBFGM+Jnu/9gD6Z59Sx8/X5PdD4VVL1eYFe2JUIWmqZsJErB26+TqCH8xqL5jaZ4TAJPF53PIVie9GwTdJfa6Jl9OD4JnmAyT3xTAE9+F8H11LGHkuqFtf4Vg5vwmKn8uv5DkxeYQ2Yac8eviceRI3CfIfW9clsZ03wVbKQAxrQ4kxyTdfBtNSxu0yJnV55J7+bFvxEZivj50ssvsz79Zgk1iksS5jBJIfvnnUH6JU06XOvN9Cl/yBJ92t4+06dpRQuz9wRt3Z53jFgKoUBTGVzx2BjMzg7CPpyB41+4so/ccf70wn+Sqb3Vb3ELZ8vtjtxRBM3i5czTbQNrDHCb/K+cX+9W/SvSuN+7SlXlDNhK0/M/MyqwZDFpLRxcJMu9nEQ6uiv5zuB1oLb5/3X3icNxUzQypuNzDeTnnG4X03oY4qOGWjg2knHIiznOYkn6/W2+eo0fTyCVn9NEOh6/PEpLzZcUcq4Vj6QVOR/fuoAPF8p2JbpXYrXE3gQ9T7pqONIP5u+ADBNAJ2ptjslWD0+61XAKwrZpEXNWS2NMraj6HSzRhHzFPG+8t8NXki5yphqRsp1OVUzUq5krZoRsvaD/Vo1I2W1JKoZJesLUuM8VkGytYdXzxZuDXuvvz23a9gr1QpqPXbXz2lV/qv8V/mv8l/lv8p/lf9+uePP775J4Ntf8Cvh758rPtogLl6B738nbB3of8isYMODYQd+/IteVQaqxznu/qVBOJDA0kEpqodX9HFhYvpRic+B6odik1WdjBno+NkndLmwg4i3WLo5AjSICZ9xdu0r5czDbNQQ+idyiGWoWSEZYoFawugoF8JnM8kCVRcivoiUdbfRIIaelsDx8zjhokeQYCmiI3iQTzXscU5kIatghvMOsuLGIeebful1yM0wxkJitOgkLiksrDznIDoCK9xUg0LXQck4Izh2dk1M6cAZLkuLMI5lrGaTLHajdOykMPdz13CqQnBbkc0cqxEKqphFDilBXFhQB6vk2sVQHRvcEjhZmK8KTFJmOTNIn0WI7kPqQw1FMZscmDncqo5bDLYja7UERqVRXSayIg+RbFa0ThQTUgonw01EjTSJsdlNBpvAmcLPwi82i40DXYIIOQTFFYcxNkyTmcQT0240dGCVEI7GSu9JNTb5FLMDyIyKIhx9mEmnEHagjtsMjlV1CMQO62Bybo1/0RIEGy/HG7J8iH/rhtVsaHX4TYYFM8S0QOywiZ1+7W47JvGsZqPqKKwAT5zRwwaUvhlRaKmvWG4nuHuF+2rWUVjYn+4/Y7ovtkGdjGE7kN07Ogn97ofiZP1fxyE2ja0AB5FdglI9vMLNdrTg1i9e4zhiPY4bzQF0KNupFlUzZzKr8Eo8WSxnBboP/h59Xvim1ToW8ZsXcG/5NXu6Oj+w2sl160hjnm5W+e8QXTJ6+iXyrM12uVbmru6DbOsjqCwF7il+2W40IdxUWIrdk+Kzwb0PdaXgPY1MG95frdW3ZTBWxmXCUn3bhlHKxrBV3zahk7IubNS3EKycK4TKf4douNxRZKeG3Vs/3OXthwvzweCrfPwYed/h27xcvHvaKWL1pJGLiKR+XABPPP8RxK26VsaYRh5+nOHNf+H+mZJ44h9B3FLIuIYsTNwx/wlELaVM0MiXATDQAH4Dz9cE3ixwgeeFO+aTuiCM50Tn9gN6difRmfzrDbCk0Z78Cu9BiuUANy2+pZgJGzlySKbzJPHfX5NsetEuPELchj938nVowj/wuMg4JD2PlsEck0nKaFzujskNgnZEwiHp1pTYloLiFrmeqMX16jtTvwmnR89M7MB8sbh/goX9a5Jof5JzBVqwbN2kw9Gw+lCdmRyx4ToPYUGQ6DEk/ZjOUdISufZQUqMCcneh64rY+FZuNy34iTzRI8EcNMjn7jw1EJiCGUxrG/RcX0D/tCZ87k3Oe0sykeu7Hab0cC22Y+vAK926V9i614fcGDCVwgQZadwxbWwGZwfR0Zv+12/q2l78/OD0FmjzEHwnbnReBdcobPlcWabSiaZtFtaZ+/t1x+vJJYkMOr8kYsNWOwN+0lDNRK1cDWiyajBkMbDaONtFsb+nBBF9vqMXF2PuLw7eMw21jG/liv/yszXpuIe/fUzRY2DnfruoPN94MR4XjQc660+1yQTZzvRJv4HI7x+mfjfPNVQytpULhCE4hDoAxmZhW6Z3Ysxsbpmu92DSOK8GFuYAMIWVObuH6AH0JnKfs+nz+lXAbqeOPXEvOWe1dDtFzHuKTjfrLmpL3lf+u1FEEbzKmLJSBK/LqawUwStZKyvFb+0H+7WyUgRXS6KsqhiuL0iFN1dBtrVXWm0i17D32hyvYU/7VO3ntir/Vf6r/Ff5r/JfpZXzf1zI4Ntf8Cvh758rPtogLl6B738nbB3of8isYMODYQd+/IteVQaqxznu/qVBjEAGSwelqB5e0ceFielHJT4Hqh+KTVZ1Mmago/mELhd2EPEWSzdHgAYx4TPOrn2lnHmYjRpC/0QOsQw1KyRDLFBLGB3lQvhsRrtQdSHii0iput9AGHpaAkcbJ1z0CBIsRXQED/Kphj1hOY4sZBXMcN5BVtw45HzTi91C/jGMsZAYLTqJSwoLK885iI7ACjfVoNB1UDLOCI6dXRNTOnCGy7qJYBzLWM0mWexG6SSsG/q5azhVIbityGaO1QgFVcwih5QgLiyog1XymwtDdWxwS+BkYb4qMEmZ5cwgfRYhug9s0tocxWxyYOZwqzpuMdiOrNUSGJVGdZnIijxEslnROlFMSCmcDDcRNdIkxmY3GWwCZwpN+MVmsXGgSxAhh6C44jDGhmkyk3hi2o2GDqwJNDRWek+qscmnmB1AZlQU4ejDTDqFsAN13GZwrKpDIHZYB5Nza/yLliDYeDnekOVD/Fs3rGZDq8NvMiyYIaYFYodN7PRrd9sxiWc1G1VHYQV44oweNqD0zYhCS33FcjvB3SvcV7OOwsL+dG+Y7ottUCdj2A5k945OQr/7oThZ/9dxiE1jK8BBZJegVA+vcLMdLbj1i5e7o7rvNw5Ah7KdalE1cyazCq/Ek8VyVqD74O/R54VvWq1jEb95AfeWX7Onq/MDq51MK98w/+9m/7OI688wVZ8zdU0nulgL6/l7tWI8LLTzXitGWurFEKdmDHHqxhCldgxR6sfgU0MGnzoy2NSSwaaejCQ1ZWjL9P7A68uq/Ff5r/Jf5b/D6Bg=)
> 
> 
> **Figure 4**
> 
> - After the ‘Slice’ op, there are series of channel convolutions, the first one 1 -&gt; 9 -&gt; 1
> (Conv2D\_1 and Conv2D\_3), and the second one 1 -&gt; 7 -&gt; 1 (Conv2D\_2 and Conv2D\_4). The outputs of
> the 2 series are then subtracted. The output of subtract op is then added to the scaled version
> of the first channel coming from ‘Slice’.
> - Instead, we can equivalently formulate this as a single series of convolutions as 1 -&gt; 16 -&gt; 2,
> followed by a convolution with [+1, -1] weights, as shown in Figure 5.
> 
> 
> 
> 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)
> 
> 
> **Figure 5**
> 
> - The result of that is then added to scaled version of other channel, which again is just
> beefing up the conv so instead of just [ +1, -1] weights you could combine with other channel
> as shown.  The weight vector in this case would be [k, +1, -1], where k is the scalar value with
> which to multiply, as shown in Figure 6 below.
> 
> 
> 
> ![../../_static/resources/htp_guidelines_fig_6.png](data:image/png;base64,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)
> 
> 
> **Figure 6**

3. Some of these transformations may not be intuitive since they theoretically increase the amount
of work required and consequently MACs, however we typically expect performance and power to be
improved in these cases due to better alignment with hardware.

4. Some of these transformations might not be lossless and might reduce accuracy but it’s
worthwhile to consider network designs of this nature ahead of time at training time.

> 
> 
> - Example: Consider the Figure 7 below
> 
> 
> 
> ![../../_static/resources/htp_guidelines_fig_7.png](data:image/png;base64,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)
> 
> 
> **Figure 7**
> 
> - We have three separate convolutions of size [418,418,6] -&gt; [3x3] -&gt; [416,416,2]. The 3 outputs
> of size [416,416,2] are then concatenated in depth to get an output of size [416,416,6].
> - For better performance, instead, it is best to concat 3 inputs of size [418,418,6] to a single
> [418,418,18]. Then a single convolution of [3x3] produces the [416,416,6] output, as shown in Figure 8.
> 
> 
> 
> ![../../_static/resources/htp_guidelines_fig_8.png](data:image/png;base64,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)
> 
> 
> **Figure 8**

Last Published: Jun 04, 2026

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