diff --git a/arm_compute/core/NEON/kernels/assembly/arm_gemm.hpp b/arm_compute/core/NEON/kernels/assembly/arm_gemm.hpp
index 828b0f2..17faab1 100644
--- a/arm_compute/core/NEON/kernels/assembly/arm_gemm.hpp
+++ b/arm_compute/core/NEON/kernels/assembly/arm_gemm.hpp
@@ -65,7 +65,21 @@
     GemmConfig() { }
 };
 
-template<typename T>
+struct Activation
+{
+    enum class Type {
+        None,
+        ReLU,
+        BoundedReLU
+    };
+
+    Type    type;
+    float   param1;
+    float   param2;
+
+    Activation(Type type=Type::None, float p1=0.0f, float p2=0.0f) : type(type), param1(p1), param2(p2) { }
+};
+
 struct GemmArgs
 {
 public:
@@ -77,8 +91,7 @@
     unsigned int      _nmulti;
     bool              _trA;
     bool              _trB;
-    T                 _alpha;
-    T                 _beta;
+    Activation        _act;
     int               _maxthreads;
     bool              _pretransposed_hint;
     const GemmConfig *_cfg;
@@ -86,10 +99,10 @@
     GemmArgs(const CPUInfo *ci, const unsigned int M, const unsigned int N,
              const unsigned int K, const unsigned int nbatches,
              const unsigned int nmulti, const bool trA, const bool trB,
-             const T alpha, const T beta, const int maxthreads,
+             Activation act, const int maxthreads,
              const bool pretransposed_hint, const GemmConfig *cfg=nullptr ) :
              _ci(ci), _Msize(M), _Nsize(N), _Ksize(K), _nbatches(nbatches), _nmulti(nmulti),
-             _trA(trA), _trB(trB), _alpha(alpha), _beta(beta), _maxthreads(maxthreads),
+             _trA(trA), _trB(trB), _act(act), _maxthreads(maxthreads),
              _pretransposed_hint(pretransposed_hint), _cfg(cfg)
     {
     }
@@ -99,6 +112,7 @@
 {
 public:
     const int32_t  *bias;
+    size_t          bias_multi_stride;
     int32_t         a_offset;
     int32_t         b_offset;
     int32_t         c_offset;
@@ -109,8 +123,8 @@
 
     ARequantizeLayer32() = default;
 
-    ARequantizeLayer32(int32_t *b, int32_t ao, int32_t bo, int32_t co, int32_t rs, int32_t rm, int32_t minv, int32_t maxv) :
-        bias(b), a_offset(ao), b_offset(bo), c_offset(co), requant_shift(rs), requant_mul(rm), minval(minv), maxval(maxv)
+    ARequantizeLayer32(const int32_t *b, size_t bms, int32_t ao, int32_t bo, int32_t co, int32_t rs, int32_t rm, int32_t minv, int32_t maxv) :
+        bias(b), bias_multi_stride(bms), a_offset(ao), b_offset(bo), c_offset(co), requant_shift(rs), requant_mul(rm), minval(minv), maxval(maxv)
     {
     }
 };
@@ -128,12 +142,12 @@
 /* get_gemm_method(): Given the templated types and provided parameters,
  * which is the preferred method to implement this GEMM?  */
 template<typename Top, typename Tret, class OutputStage = Nothing>
-KernelDescription get_gemm_method(const GemmArgs<Tret> &args, const OutputStage & ={});
+KernelDescription get_gemm_method(const GemmArgs &args, const OutputStage & ={});
 
 template<typename Top, typename Tret, class OutputStage = Nothing>
-UniqueGemmCommon<Top, Tret> gemm(const GemmArgs<Tret> &args, const OutputStage & ={});
+UniqueGemmCommon<Top, Tret> gemm(const GemmArgs &args, const OutputStage & ={});
 
 template<typename Top, typename Tret, class OutputStage = Nothing>
-std::vector<KernelDescription> get_compatible_kernels(const GemmArgs<Tret> &args, const OutputStage & ={});
+std::vector<KernelDescription> get_compatible_kernels(const GemmArgs &args, const OutputStage & ={});
 
 } // namespace arm_gemm