diff --git a/.github/workflows/ci.yml b/.github/workflows/ci.yml
index 23735ec5..f83be7ce 100644
--- a/.github/workflows/ci.yml
+++ b/.github/workflows/ci.yml
@@ -16,6 +16,7 @@ jobs:
       fail-fast: false
       matrix:
         version:
+          - '1.0'
           - '1'
           - 'nightly'
         os:
diff --git a/Project.toml b/Project.toml
index 5c5fd7fc..3318b46d 100644
--- a/Project.toml
+++ b/Project.toml
@@ -12,7 +12,6 @@ NaNMath = "77ba4419-2d1f-58cd-9bb1-8ffee604a2e3"
 Preferences = "21216c6a-2e73-6563-6e65-726566657250"
 Printf = "de0858da-6303-5e67-8744-51eddeeeb8d7"
 Random = "9a3f8284-a2c9-5f02-9a11-845980a1fd5c"
-SIMD = "fdea26ae-647d-5447-a871-4b548cad5224"
 SpecialFunctions = "276daf66-3868-5448-9aa4-cd146d93841b"
 StaticArrays = "90137ffa-7385-5640-81b9-e52037218182"
 
@@ -25,10 +24,9 @@ DiffTests = "0.0.1, 0.1"
 LogExpFunctions = "0.3"
 NaNMath = "0.2.2, 0.3"
 Preferences = "1"
-SIMD = "3"
 SpecialFunctions = "0.1, 0.2, 0.3, 0.4, 0.5, 0.6, 0.7, 0.8, 0.9, 0.10, 1.0"
 StaticArrays = "0.8.3, 0.9, 0.10, 0.11, 0.12, 1.0"
-julia = "1.6"
+julia = "1"
 
 [extras]
 Calculus = "49dc2e85-a5d0-5ad3-a950-438e2897f1b9"
diff --git a/src/ForwardDiff.jl b/src/ForwardDiff.jl
index 5174e324..93d3b246 100644
--- a/src/ForwardDiff.jl
+++ b/src/ForwardDiff.jl
@@ -8,7 +8,6 @@ if VERSION >= v"1.6"
 end
 using Random
 using LinearAlgebra
-import SIMD: Vec
 
 import Printf
 import NaNMath
@@ -16,13 +15,6 @@ import SpecialFunctions
 import LogExpFunctions
 import CommonSubexpressions
 
-const SIMDFloat = Union{Float64, Float32}
-const SIMDInt = Union{
-                       Int128, Int64, Int32, Int16, Int8,
-                       UInt128, UInt64, UInt32, UInt16, UInt8,
-                     }
-const SIMDType = Union{SIMDFloat, SIMDInt}
-
 include("prelude.jl")
 include("partials.jl")
 include("dual.jl")
diff --git a/src/dual.jl b/src/dual.jl
index 75d46378..03330c00 100644
--- a/src/dual.jl
+++ b/src/dual.jl
@@ -541,16 +541,6 @@ end
 # fma #
 #-----#
 
-@inline function calc_fma_xyz(x::Dual{T,V,N},
-                              y::Dual{T,V,N},
-                              z::Dual{T,V,N}) where {T, V<:SIMDFloat,N}
-    xv, yv, zv = value(x), value(y), value(z)
-    rv = fma(xv, yv, zv)
-    N == 0 && return Dual{T}(rv)
-    xp, yp, zp = Vec(partials(x).values), Vec(partials(y).values), Vec(partials(z).values)
-    parts = Tuple(fma(xv, yp, fma(yv, xp, zp)))
-    Dual{T}(rv, parts)
-end
 @generated function calc_fma_xyz(x::Dual{T,<:Any,N},
                                  y::Dual{T,<:Any,N},
                                  z::Dual{T,<:Any,N}) where {T,N}
@@ -593,16 +583,6 @@ end
 # muladd #
 #--------#
 
-@inline function calc_muladd_xyz(x::Dual{T,V,N},
-                                 y::Dual{T,V,N},
-                                 z::Dual{T,V,N}) where {T, V<:SIMDType,N}
-    xv, yv, zv = value(x), value(y), value(z)
-    rv = muladd(xv, yv, zv)
-    N == 0 && return Dual{T}(rv)
-    xp, yp, zp = Vec(partials(x).values), Vec(partials(y).values), Vec(partials(z).values)
-    parts = Tuple(muladd(xv, yp, muladd(yv, xp, zp)))
-    Dual{T}(rv, parts)
-end
 @generated function calc_muladd_xyz(x::Dual{T,<:Any,N},
                                     y::Dual{T,<:Any,N},
                                     z::Dual{T,<:Any,N}) where {T,N}
diff --git a/src/partials.jl b/src/partials.jl
index 7a94884e..fce67b0a 100644
--- a/src/partials.jl
+++ b/src/partials.jl
@@ -141,13 +141,6 @@ end
 @inline _mul_partials(a::Partials{0,A}, b::Partials{N,B}, afactor, bfactor) where {N,A,B} = bfactor * b
 @inline _mul_partials(a::Partials{N,A}, b::Partials{0,B}, afactor, bfactor) where {N,A,B} = afactor * a
 
-const SIMDFloat = Union{Float64, Float32}
-const SIMDInt = Union{
-                       Int128, Int64, Int32, Int16, Int8,
-                       UInt128, UInt64, UInt32, UInt16, UInt8,
-                     }
-const SIMDType = Union{SIMDFloat, SIMDInt}
-
 ##################################
 # Generated Functions on NTuples #
 ##################################
@@ -171,7 +164,6 @@ end
 @inline rand_tuple(::AbstractRNG, ::Type{Tuple{}}) = tuple()
 @inline rand_tuple(::Type{Tuple{}}) = tuple()
 
-iszero_tuple(tup::NTuple{N,V}) where {N, V<:SIMDType} = sum(Vec(tup) != zero(V)) == 0
 @generated function iszero_tuple(tup::NTuple{N,V}) where {N,V}
     ex = Expr(:&&, [:(z == tup[$i]) for i=1:N]...)
     return quote
@@ -205,24 +197,29 @@ end
     return tupexpr(i -> :(rand(V)), N)
 end
 
-const NT{N,T} = NTuple{N,T}
+@generated function scale_tuple(tup::NTuple{N}, x) where N
+    return tupexpr(i -> :(tup[$i] * x), N)
+end
 
-# SIMD implementation
-@inline add_tuples(a::NT{N,T}, b::NT{N,T})               where {N, T<:SIMDType}  = Tuple(Vec(a) + Vec(b))
-@inline sub_tuples(a::NT{N,T}, b::NT{N,T})               where {N, T<:SIMDType}  = Tuple(Vec(a) - Vec(b))
-@inline scale_tuple(tup::NT{N,T}, x::T)                  where {N, T<:SIMDType}  = Tuple(Vec(tup) * x)
-@inline div_tuple_by_scalar(tup::NT{N,T}, x::T)          where {N, T<:SIMDFloat} = Tuple(Vec(tup) / x)
-@inline minus_tuple(tup::NT{N,T})                        where {N, T<:SIMDType}  = Tuple(-Vec(tup))
-@inline mul_tuples(a::NT{N,T}, b::NT{N,T}, af::T, bf::T) where {N, T<:SIMDType}  = Tuple(muladd(af, Vec(a), bf * Vec(b)))
+@generated function div_tuple_by_scalar(tup::NTuple{N}, x) where N
+    return tupexpr(i -> :(tup[$i] / x), N)
+end
 
+@generated function add_tuples(a::NTuple{N}, b::NTuple{N})  where N
+    return tupexpr(i -> :(a[$i] + b[$i]), N)
+end
 
-# Fallback implementations
-@generated add_tuples(a::NT{N}, b::NT{N})         where N = tupexpr(i -> :(a[$i] + b[$i]), N)
-@generated sub_tuples(a::NT{N}, b::NT{N})         where N = tupexpr(i -> :(a[$i] - b[$i]), N)
-@generated scale_tuple(tup::NT{N}, x)             where N = tupexpr(i -> :(tup[$i] * x), N)
-@generated div_tuple_by_scalar(tup::NT{N}, x)     where N = tupexpr(i -> :(tup[$i] / x), N)
-@generated minus_tuple(tup::NT{N})                where N = tupexpr(i -> :(-tup[$i]), N)
-@generated mul_tuples(a::NT{N}, b::NT{N}, af, bf) where N = tupexpr(i -> :(muladd(af, a[$i], bf * b[$i])), N)
+@generated function sub_tuples(a::NTuple{N}, b::NTuple{N})  where N
+    return tupexpr(i -> :(a[$i] - b[$i]), N)
+end
+
+@generated function minus_tuple(tup::NTuple{N}) where N
+    return tupexpr(i -> :(-tup[$i]), N)
+end
+
+@generated function mul_tuples(a::NTuple{N}, b::NTuple{N}, afactor, bfactor) where N
+    return tupexpr(i -> :((afactor * a[$i]) + (bfactor * b[$i])), N)
+end
 
 ###################
 # Pretty Printing #
diff --git a/test/PartialsTest.jl b/test/PartialsTest.jl
index 08e76bda..39fb05d7 100644
--- a/test/PartialsTest.jl
+++ b/test/PartialsTest.jl
@@ -114,7 +114,7 @@ for N in (0, 3), T in (Int, Float32, Float64)
 
     if N > 0
         @test ForwardDiff._div_partials(PARTIALS, PARTIALS2, X, Y) == ForwardDiff._mul_partials(PARTIALS, PARTIALS2, inv(Y), -X/(Y^2))
-        @test all(isapprox.(ForwardDiff._mul_partials(PARTIALS, PARTIALS2, X, Y).values, map((a, b) -> (X * a) + (Y * b), VALUES, VALUES2)))
+        @test ForwardDiff._mul_partials(PARTIALS, PARTIALS2, X, Y).values == map((a, b) -> (X * a) + (Y * b), VALUES, VALUES2)
         @test ForwardDiff._mul_partials(ZERO_PARTIALS, PARTIALS, X, Y) == Y * PARTIALS
         @test ForwardDiff._mul_partials(PARTIALS, ZERO_PARTIALS, X, Y) == X * PARTIALS