diff --git a/examples/notebooks/data_attest.ipynb b/examples/notebooks/data_attest.ipynb
index 6cc3759b6..d361037f3 100644
--- a/examples/notebooks/data_attest.ipynb
+++ b/examples/notebooks/data_attest.ipynb
@@ -592,7 +592,7 @@
             "name": "python",
             "nbconvert_exporter": "python",
             "pygments_lexer": "ipython3",
-            "version": "3.12.2"
+            "version": "3.12.5"
         },
         "orig_nbformat": 4
     },
diff --git a/examples/notebooks/data_attest_hashed.ipynb b/examples/notebooks/data_attest_hashed.ipynb
index bd6979dd4..de8e58800 100644
--- a/examples/notebooks/data_attest_hashed.ipynb
+++ b/examples/notebooks/data_attest_hashed.ipynb
@@ -648,10 +648,10 @@
             "name": "python",
             "nbconvert_exporter": "python",
             "pygments_lexer": "ipython3",
-            "version": "3.9.15"
+            "version": "3.12.5"
         },
         "orig_nbformat": 4
     },
     "nbformat": 4,
     "nbformat_minor": 2
-}
\ No newline at end of file
+}
diff --git a/examples/notebooks/logistic_regression.ipynb b/examples/notebooks/logistic_regression.ipynb
index 0bdd16820..b6c30c10b 100644
--- a/examples/notebooks/logistic_regression.ipynb
+++ b/examples/notebooks/logistic_regression.ipynb
@@ -271,7 +271,7 @@
             "name": "python",
             "nbconvert_exporter": "python",
             "pygments_lexer": "ipython3",
-            "version": "3.12.2"
+            "version": "3.12.7"
         }
     },
     "nbformat": 4,
diff --git a/examples/onnx/log/gen.py b/examples/onnx/log/gen.py
new file mode 100644
index 000000000..fba681496
--- /dev/null
+++ b/examples/onnx/log/gen.py
@@ -0,0 +1,42 @@
+from torch import nn
+import torch
+import json
+import numpy as np
+
+
+class MyModel(nn.Module):
+    def __init__(self):
+        super(MyModel, self).__init__()
+
+    def forward(self, x):          
+        m = torch.log(x)
+        
+        return m 
+
+
+circuit = MyModel()
+
+x = torch.empty(1, 8).uniform_(0, 3)
+
+out = circuit(x)
+
+print(out)
+
+torch.onnx.export(circuit, x, "network.onnx",
+                  export_params=True,        # store the trained parameter weights inside the model file
+                  opset_version=17,          # the ONNX version to export the model to
+                  do_constant_folding=True,  # whether to execute constant folding for optimization
+                  input_names=['input'],   # the model's input names
+                  output_names=['output'],  # the model's output names
+                  dynamic_axes={'input': {0: 'batch_size'},  # variable length axes
+                                'output': {0: 'batch_size'}})
+
+
+d1 = ((x).detach().numpy()).reshape([-1]).tolist()
+
+data = dict(
+    input_data=[d1],
+)
+
+# Serialize data into file:
+json.dump(data, open("input.json", 'w'))
diff --git a/examples/onnx/log/input.json b/examples/onnx/log/input.json
new file mode 100644
index 000000000..f9139376c
--- /dev/null
+++ b/examples/onnx/log/input.json
@@ -0,0 +1 @@
+{"input_data": [[1.9252371788024902, 1.8418371677398682, 0.8400403261184692, 2.083845853805542, 0.9760497808456421, 0.6940176486968994, 0.015579521656036377, 2.2689192295074463]]}
\ No newline at end of file
diff --git a/examples/onnx/log/network.onnx b/examples/onnx/log/network.onnx
new file mode 100644
index 000000000..43233b518
--- /dev/null
+++ b/examples/onnx/log/network.onnx
@@ -0,0 +1,14 @@
+pytorch2.2.2:o
+
+inputoutput/Log"Log
+main_graphZ!
+input
+

+
+batch_size
+b"
+output
+

+
+batch_size
+B
\ No newline at end of file
diff --git a/src/bindings/python.rs b/src/bindings/python.rs
index 6c3be3077..59fa4aa26 100644
--- a/src/bindings/python.rs
+++ b/src/bindings/python.rs
@@ -197,6 +197,9 @@ struct PyRunArgs {
     /// int: The number of legs used for decomposition
     #[pyo3(get, set)]
     pub decomp_legs: usize,
+    /// bool: Should the circuit use unbounded lookups for log
+    #[pyo3(get, set)]
+    pub bounded_log_lookup: bool,
 }
 
 /// default instantiation of PyRunArgs
@@ -212,6 +215,7 @@ impl PyRunArgs {
 impl From<PyRunArgs> for RunArgs {
     fn from(py_run_args: PyRunArgs) -> Self {
         RunArgs {
+            bounded_log_lookup: py_run_args.bounded_log_lookup,
             tolerance: Tolerance::from(py_run_args.tolerance),
             input_scale: py_run_args.input_scale,
             param_scale: py_run_args.param_scale,
@@ -236,6 +240,7 @@ impl From<PyRunArgs> for RunArgs {
 impl Into<PyRunArgs> for RunArgs {
     fn into(self) -> PyRunArgs {
         PyRunArgs {
+            bounded_log_lookup: self.bounded_log_lookup,
             tolerance: self.tolerance.val,
             input_scale: self.input_scale,
             param_scale: self.param_scale,
diff --git a/src/circuit/ops/hybrid.rs b/src/circuit/ops/hybrid.rs
index 0ea1327c7..2bf6b2c11 100644
--- a/src/circuit/ops/hybrid.rs
+++ b/src/circuit/ops/hybrid.rs
@@ -13,6 +13,10 @@ use serde::{Deserialize, Serialize};
 /// An enum representing the operations that consist of both lookups and arithmetic operations.
 #[derive(Clone, Debug, Serialize, Deserialize)]
 pub enum HybridOp {
+    Ln {
+        scale: utils::F32,
+    },
+
     RoundHalfToEven {
         scale: utils::F32,
         legs: usize,
@@ -112,6 +116,7 @@ impl<F: PrimeField + TensorType + PartialOrd + std::hash::Hash> Op<F> for Hybrid
 
     fn as_string(&self) -> String {
         match self {
+            HybridOp::Ln { scale } => format!("LN(scale={})", scale),
             HybridOp::RoundHalfToEven { scale, legs } => {
                 format!("ROUND_HALF_TO_EVEN(scale={}, legs={})", scale, legs)
             }
@@ -189,6 +194,7 @@ impl<F: PrimeField + TensorType + PartialOrd + std::hash::Hash> Op<F> for Hybrid
         values: &[ValTensor<F>],
     ) -> Result<Option<ValTensor<F>>, CircuitError> {
         Ok(Some(match self {
+            HybridOp::Ln { scale } => layouts::ln(config, region, values[..].try_into()?, *scale)?,
             HybridOp::RoundHalfToEven { scale, legs } => {
                 layouts::round_half_to_even(config, region, values[..].try_into()?, *scale, *legs)?
             }
@@ -327,6 +333,9 @@ impl<F: PrimeField + TensorType + PartialOrd + std::hash::Hash> Op<F> for Hybrid
             HybridOp::Softmax { output_scale, .. } | HybridOp::Recip { output_scale, .. } => {
                 multiplier_to_scale(output_scale.0 as f64)
             }
+            HybridOp::Ln {
+                scale: output_scale,
+            } => 4 * multiplier_to_scale(output_scale.0 as f64),
             _ => in_scales[0],
         };
         Ok(scale)
diff --git a/src/circuit/ops/layouts.rs b/src/circuit/ops/layouts.rs
index 2f3d9a31f..85d878bcf 100644
--- a/src/circuit/ops/layouts.rs
+++ b/src/circuit/ops/layouts.rs
@@ -4507,6 +4507,332 @@ pub fn ceil<F: PrimeField + TensorType + PartialOrd + std::hash::Hash>(
     )
 }
 
+/// integer ln layout
+/// # Arguments
+/// * `config` - BaseConfig
+/// * `region` - RegionCtx
+/// * `values` - &[ValTensor<F>; 1]
+/// * `scale` - utils::F32
+/// # Returns
+/// * ValTensor<F>
+/// # Example
+///
+/// ```
+/// use ezkl::tensor::Tensor;
+/// use ezkl::fieldutils::IntegerRep;
+/// use ezkl::circuit::ops::layouts::ln;
+/// use ezkl::tensor::val::ValTensor;
+/// use halo2curves::bn256::Fr as Fp;
+/// use ezkl::circuit::region::RegionCtx;
+/// use ezkl::circuit::region::RegionSettings;
+/// use ezkl::circuit::BaseConfig;
+/// let dummy_config = BaseConfig::dummy(12, 2);
+/// let mut dummy_region = RegionCtx::new_dummy(0,2,RegionSettings::all_true(128,2));
+/// let x = ValTensor::from_integer_rep_tensor(Tensor::<IntegerRep>::new(
+/// Some(&[3, 2, 3, 1]),
+/// &[1, 1, 2, 2],
+/// ).unwrap());
+///
+/// let result = ln::<Fp>(&dummy_config, &mut dummy_region, &[x], 2.0.into()).unwrap();
+/// let expected = Tensor::<IntegerRep>::new(Some(&[4, 0, 4, -8]), &[1, 1, 2, 2]).unwrap();
+/// assert_eq!(result.int_evals().unwrap(), expected);
+///
+/// ```
+pub fn ln<F: PrimeField + TensorType + PartialOrd + std::hash::Hash>(
+    config: &BaseConfig<F>,
+    region: &mut RegionCtx<F>,
+    values: &[ValTensor<F>; 1],
+    scale: utils::F32,
+) -> Result<ValTensor<F>, CircuitError> {
+    // first generate the claimed val
+
+    let mut input = values[0].clone();
+
+    println!("input {}", input.show());
+
+    let scale_as_felt = integer_rep_to_felt(scale.0.round() as IntegerRep);
+
+    let assigned_triple_scaled_as_felt_tensor = region.assign(
+        &config.custom_gates.inputs[1],
+        &create_constant_tensor(scale_as_felt * scale_as_felt * scale_as_felt, 1),
+    )?;
+
+    // natural ln is log2(x) * ln(2)
+    let ln2 = utils::F32::from(2.0_f32.ln());
+    // now create a constant tensor for ln2 with scale
+    let ln2_tensor: ValTensor<F> = create_constant_tensor(
+        integer_rep_to_felt((ln2.0 * scale.0).round() as IntegerRep),
+        1,
+    );
+    region.assign(&config.custom_gates.inputs[0], &ln2_tensor)?;
+    let unit = create_constant_tensor(integer_rep_to_felt(1), 1);
+    region.assign(&config.custom_gates.inputs[1], &unit)?;
+    region.increment(1);
+
+    // 2. assign the image
+    if !input.all_prev_assigned() {
+        input = region.assign(&config.custom_gates.inputs[0], &input)?;
+        // don't need to increment because the claimed output is assigned to output and incremented accordingly
+    }
+
+    let is_assigned = !input.any_unknowns()?;
+
+    let mut claimed_output: ValTensor<F> = if is_assigned {
+        let input_evals = input.int_evals()?;
+        // returns an integer with the base 2 logarithm
+        tensor::ops::nonlinearities::ilog2(&input_evals.clone(), scale.0 as f64)
+            .par_iter()
+            .map(|x| Value::known(integer_rep_to_felt(*x)))
+            .collect::<Tensor<Value<F>>>()
+            .into()
+    } else {
+        Tensor::new(
+            Some(&vec![Value::<F>::unknown(); input.len()]),
+            &[input.len()],
+        )?
+        .into()
+    };
+    claimed_output.reshape(input.dims())?;
+    region.assign(&config.custom_gates.output, &claimed_output)?;
+    region.increment(claimed_output.len());
+
+    let pow2_of_claimed_output = nonlinearity(
+        config,
+        region,
+        &[claimed_output.clone()],
+        &LookupOp::PowersOfTwo { scale },
+    )?;
+
+    let num_bits = (std::mem::size_of::<IntegerRep>() * 8) as IntegerRep;
+
+    region.update_max_min_lookup_inputs_force(-num_bits, num_bits)?;
+
+    // now subtract 1 from the claimed output
+    let claimed_output_minus_one = pairwise(
+        config,
+        region,
+        &[claimed_output.clone(), unit.clone()],
+        BaseOp::Sub,
+    )?;
+
+    // now add 1 to the claimed output
+    let claimed_output_plus_one = pairwise(
+        config,
+        region,
+        &[claimed_output.clone(), unit.clone()],
+        BaseOp::Add,
+    )?;
+
+    // prior power of 2 is less than claimed output
+    let prior_pow2 = nonlinearity(
+        config,
+        region,
+        &[claimed_output_minus_one],
+        &LookupOp::PowersOfTwo { scale },
+    )?;
+
+    // next power of 2 is greater than claimed output
+    let next_pow2 = nonlinearity(
+        config,
+        region,
+        &[claimed_output_plus_one],
+        &LookupOp::PowersOfTwo { scale },
+    )?;
+
+    // assert that the original input is closest to the claimed output than the prior power of 2 and the next power of 2
+    let distance_to_prior = pairwise(
+        config,
+        region,
+        &[input.clone(), prior_pow2.clone()],
+        BaseOp::Sub,
+    )?;
+
+    // now take abs of the distance
+    let distance_to_prior_l1 = abs(config, region, &[distance_to_prior.clone()])?;
+
+    let distance_to_next = pairwise(
+        config,
+        region,
+        &[input.clone(), next_pow2.clone()],
+        BaseOp::Sub,
+    )?;
+
+    // now take abs of the distance
+    let distance_to_next_l1 = abs(config, region, &[distance_to_next.clone()])?;
+
+    let distance_to_claimed = pairwise(
+        config,
+        region,
+        &[input.clone(), pow2_of_claimed_output.clone()],
+        BaseOp::Sub,
+    )?;
+
+    // now take abs of the distance
+    let distance_to_claimed_l1 = abs(config, region, &[distance_to_claimed.clone()])?;
+
+    // can be less than or equal because we round up
+    let is_distance_to_prior_less = less_equal(
+        config,
+        region,
+        &[distance_to_claimed_l1.clone(), distance_to_prior_l1.clone()],
+    )?;
+
+    // should be striclty less because we round up
+    let is_distance_to_next_less = less(
+        config,
+        region,
+        &[distance_to_claimed_l1, distance_to_next_l1.clone()],
+    )?;
+
+    let is_distance_to_prior_less_and_distance_to_next_less = and(
+        config,
+        region,
+        &[
+            is_distance_to_prior_less.clone(),
+            is_distance_to_next_less.clone(),
+        ],
+    )?;
+
+    let mut comparison_unit = create_constant_tensor(
+        integer_rep_to_felt(1),
+        is_distance_to_prior_less_and_distance_to_next_less.len(),
+    );
+
+    comparison_unit.reshape(is_distance_to_prior_less_and_distance_to_next_less.dims())?;
+
+    // assigned unit
+    let assigned_unit = region.assign(&config.custom_gates.inputs[1], &comparison_unit)?;
+    region.increment(assigned_unit.len());
+
+    // assert that the values are truthy
+    enforce_equality(
+        config,
+        region,
+        &[
+            is_distance_to_prior_less_and_distance_to_next_less,
+            assigned_unit.clone(),
+        ],
+    )?;
+
+    // get a linear interpolation now
+
+    let sign_of_distance_to_claimed = sign(config, region, &[distance_to_claimed.clone()])?;
+    let sign_of_distance_to_claimed_is_positive = equals(
+        config,
+        region,
+        &[sign_of_distance_to_claimed.clone(), assigned_unit.clone()],
+    )?;
+
+    let sign_of_distance_to_claimed_is_negative = not(
+        config,
+        region,
+        &[sign_of_distance_to_claimed_is_positive.clone()],
+    )?;
+
+    let pow2_prior_to_claimed_distance = pairwise(
+        config,
+        region,
+        &[pow2_of_claimed_output.clone(), prior_pow2.clone()],
+        BaseOp::Sub,
+    )?;
+
+    let pow2_next_to_claimed_distance = pairwise(
+        config,
+        region,
+        &[next_pow2.clone(), pow2_of_claimed_output.clone()],
+        BaseOp::Sub,
+    )?;
+
+    let recip_pow2_prior_to_claimed_distance = recip(
+        config,
+        region,
+        &[pow2_prior_to_claimed_distance],
+        scale_as_felt,
+        scale_as_felt * scale_as_felt,
+    )?;
+
+    let interpolated_distance = pairwise(
+        config,
+        region,
+        &[
+            recip_pow2_prior_to_claimed_distance.clone(),
+            distance_to_claimed.clone(),
+        ],
+        BaseOp::Mult,
+    )?;
+
+    let gated_prior_interpolated_distance = pairwise(
+        config,
+        region,
+        &[
+            interpolated_distance.clone(),
+            sign_of_distance_to_claimed_is_negative.clone(),
+        ],
+        BaseOp::Mult,
+    )?;
+
+    let recip_next_to_claimed_distance = recip(
+        config,
+        region,
+        &[pow2_next_to_claimed_distance],
+        scale_as_felt,
+        scale_as_felt * scale_as_felt,
+    )?;
+
+    let interpolated_distance_next = pairwise(
+        config,
+        region,
+        &[
+            recip_next_to_claimed_distance.clone(),
+            distance_to_claimed.clone(),
+        ],
+        BaseOp::Mult,
+    )?;
+
+    let gated_next_interpolated_distance = pairwise(
+        config,
+        region,
+        &[
+            interpolated_distance_next.clone(),
+            sign_of_distance_to_claimed_is_positive.clone(),
+        ],
+        BaseOp::Mult,
+    )?;
+
+    let scaled_claimed_output = pairwise(
+        config,
+        region,
+        &[
+            claimed_output.clone(),
+            assigned_triple_scaled_as_felt_tensor,
+        ],
+        BaseOp::Mult,
+    )?;
+
+    let claimed_output = pairwise(
+        config,
+        region,
+        &[
+            scaled_claimed_output.clone(),
+            gated_prior_interpolated_distance.clone(),
+        ],
+        BaseOp::Add,
+    )?;
+
+    let claimed_output = pairwise(
+        config,
+        region,
+        &[
+            claimed_output.clone(),
+            gated_next_interpolated_distance.clone(),
+        ],
+        BaseOp::Add,
+    )?;
+
+    // now multiply the claimed output by ln2
+    pairwise(config, region, &[claimed_output, ln2_tensor], BaseOp::Mult)
+}
+
 /// round layout
 /// # Arguments
 /// * `config` - BaseConfig
diff --git a/src/circuit/ops/lookup.rs b/src/circuit/ops/lookup.rs
index 0f7ca0852..b6f70679d 100644
--- a/src/circuit/ops/lookup.rs
+++ b/src/circuit/ops/lookup.rs
@@ -16,10 +16,11 @@ use halo2curves::ff::PrimeField;
 pub enum LookupOp {
     Div { denom: utils::F32 },
     IsOdd,
+    PowersOfTwo { scale: utils::F32 },
+    Ln { scale: utils::F32 },
     Sqrt { scale: utils::F32 },
     Rsqrt { scale: utils::F32 },
     Sigmoid { scale: utils::F32 },
-    Ln { scale: utils::F32 },
     Exp { scale: utils::F32 },
     Cos { scale: utils::F32 },
     ACos { scale: utils::F32 },
@@ -50,6 +51,8 @@ impl LookupOp {
     pub fn as_path(&self) -> String {
         match self {
             LookupOp::Pow { scale, a } => format!("pow_{}_{}", scale, a),
+            LookupOp::Ln { scale } => format!("ln_{}", scale),
+            LookupOp::PowersOfTwo { scale } => format!("pow2_{}", scale),
             LookupOp::IsOdd => "is_odd".to_string(),
             LookupOp::Div { denom } => format!("div_{}", denom),
             LookupOp::Sigmoid { scale } => format!("sigmoid_{}", scale),
@@ -57,7 +60,6 @@ impl LookupOp {
             LookupOp::Rsqrt { scale } => format!("rsqrt_{}", scale),
             LookupOp::Erf { scale } => format!("erf_{}", scale),
             LookupOp::Exp { scale } => format!("exp_{}", scale),
-            LookupOp::Ln { scale } => format!("ln_{}", scale),
             LookupOp::Cos { scale } => format!("cos_{}", scale),
             LookupOp::ACos { scale } => format!("acos_{}", scale),
             LookupOp::Cosh { scale } => format!("cosh_{}", scale),
@@ -82,6 +84,12 @@ impl LookupOp {
         let x = x[0].clone().map(|x| felt_to_integer_rep(x));
         let res =
             match &self {
+                LookupOp::Ln { scale } => {
+                    Ok::<_, TensorError>(tensor::ops::nonlinearities::ln(&x, scale.into()))
+                }
+                LookupOp::PowersOfTwo { scale } => {
+                    Ok::<_, TensorError>(tensor::ops::nonlinearities::ipow2(&x, scale.0.into()))
+                }
                 LookupOp::IsOdd => Ok::<_, TensorError>(tensor::ops::nonlinearities::is_odd(&x)),
                 LookupOp::Pow { scale, a } => Ok::<_, TensorError>(
                     tensor::ops::nonlinearities::pow(&x, scale.0.into(), a.0.into()),
@@ -104,9 +112,6 @@ impl LookupOp {
                 LookupOp::Exp { scale } => {
                     Ok::<_, TensorError>(tensor::ops::nonlinearities::exp(&x, scale.into()))
                 }
-                LookupOp::Ln { scale } => {
-                    Ok::<_, TensorError>(tensor::ops::nonlinearities::ln(&x, scale.into()))
-                }
                 LookupOp::Cos { scale } => {
                     Ok::<_, TensorError>(tensor::ops::nonlinearities::cos(&x, scale.into()))
                 }
@@ -163,10 +168,11 @@ impl<F: PrimeField + TensorType + PartialOrd + std::hash::Hash> Op<F> for Lookup
     /// Returns the name of the operation
     fn as_string(&self) -> String {
         match self {
+            LookupOp::Ln { scale } => format!("LN(scale={})", scale),
+            LookupOp::PowersOfTwo { scale } => format!("POWERS_OF_TWO(scale={})", scale),
             LookupOp::IsOdd => "IS_ODD".to_string(),
             LookupOp::Pow { a, scale } => format!("POW(scale={}, exponent={})", scale, a),
             LookupOp::Div { denom, .. } => format!("DIV(denom={})", denom),
-            LookupOp::Ln { scale } => format!("LN(scale={})", scale),
             LookupOp::Sigmoid { scale } => format!("SIGMOID(scale={})", scale),
             LookupOp::Sqrt { scale } => format!("SQRT(scale={})", scale),
             LookupOp::Erf { scale } => format!("ERF(scale={})", scale),
diff --git a/src/graph/utilities.rs b/src/graph/utilities.rs
index ae89df0a7..f2379b273 100644
--- a/src/graph/utilities.rs
+++ b/src/graph/utilities.rs
@@ -851,9 +851,18 @@ pub fn new_op_from_onnx(
         "Exp" => SupportedOp::Nonlinear(LookupOp::Exp {
             scale: scale_to_multiplier(input_scales[0]).into(),
         }),
-        "Ln" => SupportedOp::Nonlinear(LookupOp::Ln {
-            scale: scale_to_multiplier(input_scales[0]).into(),
-        }),
+        "Ln" => {
+            if run_args.bounded_log_lookup {
+                SupportedOp::Hybrid(HybridOp::Ln {
+                    scale: scale_to_multiplier(input_scales[0]).into(),
+                })
+            } else {
+                SupportedOp::Nonlinear(LookupOp::Ln {
+                    scale: scale_to_multiplier(input_scales[0]).into(),
+                })
+            }
+        }
+
         "Sin" => SupportedOp::Nonlinear(LookupOp::Sin {
             scale: scale_to_multiplier(input_scales[0]).into(),
         }),
diff --git a/src/lib.rs b/src/lib.rs
index ffa770699..329ecd30b 100644
--- a/src/lib.rs
+++ b/src/lib.rs
@@ -317,11 +317,18 @@ pub struct RunArgs {
     #[cfg_attr(all(feature = "ezkl", not(target_arch = "wasm32")), arg(long, default_value = "2", value_hint = clap::ValueHint::Other))]
     /// the number of legs used for decompositions
     pub decomp_legs: usize,
+    #[cfg_attr(
+        all(feature = "ezkl", not(target_arch = "wasm32")),
+        arg(long, default_value = "false")
+    )]
+    /// use unbounded lookup for the log
+    pub bounded_log_lookup: bool,
 }
 
 impl Default for RunArgs {
     fn default() -> Self {
         Self {
+            bounded_log_lookup: false,
             tolerance: Tolerance::default(),
             input_scale: 7,
             param_scale: 7,
diff --git a/src/tensor/ops.rs b/src/tensor/ops.rs
index bdd79f651..79f8eb8a7 100644
--- a/src/tensor/ops.rs
+++ b/src/tensor/ops.rs
@@ -1500,6 +1500,59 @@ pub mod nonlinearities {
         .unwrap()
     }
 
+    /// Powers of 2
+    /// # Arguments
+    /// * `a` - Tensor
+    /// * `scale` - Single value
+    /// # Examples
+    /// ```
+    /// use ezkl::tensor::Tensor;
+    /// use ezkl::fieldutils::IntegerRep;
+    /// use ezkl::tensor::ops::nonlinearities::ipow2;
+    /// let x = Tensor::<IntegerRep>::new(
+    ///  Some(&[2, 15, 2, 1, 1, 0]),
+    /// &[2, 3],
+    /// ).unwrap();
+    /// let result = ipow2(&x, 1.0);
+    /// let expected = Tensor::<IntegerRep>::new(Some(&[4, 32768, 4, 2, 2, 1]), &[2, 3]).unwrap();
+    /// assert_eq!(result, expected);
+    /// ```
+    pub fn ipow2(a: &Tensor<IntegerRep>, scale_output: f64) -> Tensor<IntegerRep> {
+        a.par_enum_map(|_, a_i| {
+            let kix = a_i as f64;
+            let kix = scale_output * (2.0_f64).powf(kix);
+            let rounded = kix.round();
+            Ok::<_, TensorError>(rounded as IntegerRep)
+        })
+        .unwrap()
+    }
+
+    /// Elementwise applies ln base 2 to a tensor of integers.
+    /// # Arguments
+    /// * `a` - Tensor
+    /// * `scale_input` - Single value
+    /// ```
+    /// use ezkl::tensor::Tensor;
+    /// use ezkl::fieldutils::IntegerRep;
+    /// use ezkl::tensor::ops::nonlinearities::ilog2;
+    /// let x = Tensor::<IntegerRep>::new(
+    ///    Some(&[2, 15, 2, 1, 1, 2]),
+    /// &[2, 3],
+    /// ).unwrap();
+    /// let result = ilog2(&x, 1.0);
+    /// let expected = Tensor::<IntegerRep>::new(Some(&[1, 4, 1, 0, 0, 1]), &[2, 3]).unwrap();
+    /// assert_eq!(result, expected);
+    /// ```
+    pub fn ilog2(a: &Tensor<IntegerRep>, scale_input: f64) -> Tensor<IntegerRep> {
+        a.par_enum_map(|_, a_i| {
+            let kix = (a_i as f64) / scale_input;
+            let kix = (kix).log2();
+            let rounded = kix.round();
+            Ok::<_, TensorError>(rounded as IntegerRep)
+        })
+        .unwrap()
+    }
+
     /// Elementwise applies sigmoid to a tensor of integers.
     /// # Arguments
     ///
@@ -1628,12 +1681,11 @@ pub mod nonlinearities {
         .unwrap()
     }
 
-    /// Elementwise applies exponential to a tensor of integers.
+    /// Elementwise applies ln to a tensor of integers.
     /// # Arguments
     ///
     /// * `a` - Tensor
     /// * `scale_input` - Single value
-    /// * `scale_output` - Single value
     /// # Examples
     /// ```
     /// use ezkl::tensor::Tensor;
diff --git a/tests/assets/model.compiled b/tests/assets/model.compiled
index d1a3d508a..b665de881 100644
Binary files a/tests/assets/model.compiled and b/tests/assets/model.compiled differ
diff --git a/tests/assets/proof.json b/tests/assets/proof.json
index 6db6c2445..3a826cc00 100644
--- a/tests/assets/proof.json
+++ b/tests/assets/proof.json
@@ -1 +1 @@
-{"protocol":null,"instances":[["0000000000000000000000000000000000000000000000000000000000000000","0000000000000000000000000000000000000000000000000000000000000000","0000000000000000000000000000000000000000000000000000000000000000","0000000000000000000000000000000000000000000000000000000000000000"]],"proof":[7,102,87,190,153,229,59,6,250,221,10,63,210,51,26,207,46,194,145,97,237,1,245,42,248,173,49,189,21,0,216,125,34,9,199,125,171,108,12,101,89,75,31,133,179,147,239,237,218,207,81,198,82,129,144,247,68,204,166,44,217,66,221,35,17,33,29,160,129,121,229,44,82,145,52,107,213,158,203,98,129,56,97,204,34,80,245,3,34,5,252,31,167,58,56,2,46,210,50,211,53,35,250,143,122,31,14,84,171,83,208,117,236,3,97,98,59,102,89,161,167,18,194,233,26,45,58,38,42,83,206,156,150,9,69,180,201,190,62,248,116,104,6,168,207,112,166,244,168,160,60,83,250,74,40,63,19,85,44,27,30,165,252,35,212,69,204,145,255,18,204,249,66,245,192,116,111,38,175,106,252,101,33,182,129,200,188,246,224,111,18,116,29,136,21,25,83,69,48,217,153,96,162,102,210,156,188,8,183,175,181,194,45,244,231,154,102,19,18,152,102,167,34,175,2,92,46,210,136,40,78,202,251,77,209,104,254,116,21,37,168,44,135,119,129,216,252,171,70,50,243,103,194,115,18,125,22,42,29,183,17,110,66,169,83,226,46,203,216,91,21,148,167,252,33,157,201,156,20,233,149,244,87,184,248,255,14,144,44,49,13,249,77,141,69,211,180,110,63,67,91,75,235,138,88,93,74,113,253,66,111,192,243,132,182,86,184,22,135,179,2,112,0,135,178,191,162,208,159,58,198,75,119,147,118,211,123,5,216,206,242,48,38,98,156,215,28,70,97,33,224,149,28,145,250,188,1,52,183,235,236,117,6,140,80,29,215,31,254,156,17,10,29,184,124,55,228,139,63,193,3,207,68,16,9,207,169,136,53,34,166,195,225,22,236,10,200,246,61,4,236,31,71,161,12,17,126,135,26,197,8,101,142,82,231,57,44,76,64,86,37,222,181,85,166,186,2,138,108,70,116,45,60,86,220,44,23,240,162,185,141,196,147,50,163,42,197,7,29,215,253,51,30,13,160,202,14,34,89,185,112,183,170,9,43,64,87,86,87,223,238,221,185,181,181,105,132,245,167,217,24,206,84,81,109,69,112,31,14,90,22,99,59,222,83,190,241,72,86,103,39,90,98,201,42,29,5,149,233,120,234,57,42,29,23,75,127,138,84,57,241,193,71,212,213,184,25,163,131,79,55,28,182,52,178,65,193,214,211,84,24,52,155,247,21,200,242,170,146,244,46,164,38,166,5,201,19,214,103,89,20,8,5,173,157,189,211,53,137,20,32,222,97,102,44,188,29,215,253,51,30,13,160,202,14,34,89,185,112,183,170,9,43,64,87,86,87,223,238,221,185,181,181,105,132,245,167,217,24,206,84,81,109,69,112,31,14,90,22,99,59,222,83,190,241,72,86,103,39,90,98,201,42,29,5,149,233,120,234,57,29,215,253,51,30,13,160,202,14,34,89,185,112,183,170,9,43,64,87,86,87,223,238,221,185,181,181,105,132,245,167,217,24,206,84,81,109,69,112,31,14,90,22,99,59,222,83,190,241,72,86,103,39,90,98,201,42,29,5,149,233,120,234,57,29,215,253,51,30,13,160,202,14,34,89,185,112,183,170,9,43,64,87,86,87,223,238,221,185,181,181,105,132,245,167,217,24,206,84,81,109,69,112,31,14,90,22,99,59,222,83,190,241,72,86,103,39,90,98,201,42,29,5,149,233,120,234,57,29,215,253,51,30,13,160,202,14,34,89,185,112,183,170,9,43,64,87,86,87,223,238,221,185,181,181,105,132,245,167,217,24,206,84,81,109,69,112,31,14,90,22,99,59,222,83,190,241,72,86,103,39,90,98,201,42,29,5,149,233,120,234,57,5,97,158,236,112,200,133,205,196,108,135,251,243,47,53,102,214,117,81,123,223,159,154,3,168,145,32,206,56,130,244,126,30,123,25,247,42,138,80,13,204,246,88,185,216,221,11,188,68,43,30,137,242,63,67,148,239,99,199,15,250,236,239,178,26,209,34,200,138,140,206,3,67,51,120,204,73,126,249,188,48,66,238,66,204,200,133,243,45,13,1,207,81,184,238,53,33,84,61,78,74,147,16,193,71,46,210,204,179,17,101,87,102,136,143,222,76,225,178,69,62,245,63,20,142,102,17,94,43,74,104,11,146,4,125,21,142,126,135,189,149,143,205,140,34,111,163,62,109,210,31,204,175,50,7,217,26,191,190,60,37,94,107,29,204,10,222,124,188,245,129,125,217,165,220,135,35,91,49,229,85,96,27,25,99,9,225,32,116,153,215,134,13,183,224,204,233,128,102,238,97,14,254,157,39,96,4,145,139,222,112,155,177,102,13,204,135,28,105,114,71,135,178,11,37,61,49,167,12,140,165,109,159,47,215,57,192,92,118,228,228,54,109,17,225,220,56,103,249,249,180,156,65,141,223,161,38,42,160,63,164,253,134,50,29,64,123,102,101,110,56,229,245,8,229,190,244,56,138,30,64,224,126,14,113,160,14,77,9,45,12,94,228,153,98,165,193,202,194,26,128,181,157,153,242,225,127,254,16,186,93,108,0,238,185,30,194,230,7,12,18,221,99,250,238,234,234,195,200,50,226,31,74,56,205,29,227,45,40,5,203,46,50,5,158,63,195,133,196,246,132,193,3,10,109,191,2,167,137,30,58,26,46,165,32,122,120,160,79,101,189,105,241,63,168,45,233,249,123,111,33,198,5,1,18,221,99,250,238,234,234,195,200,50,226,31,74,56,205,29,227,45,40,5,203,46,50,5,158,63,195,133,196,246,132,193,3,10,109,191,2,167,137,30,58,26,46,165,32,122,120,160,79,101,189,105,241,63,168,45,233,249,123,111,33,198,5,1,36,210,211,5,222,163,212,162,90,216,137,227,17,182,242,62,205,101,157,131,103,67,158,89,10,55,203,240,128,163,67,46,11,84,104,82,21,171,88,223,111,11,21,150,190,181,104,80,103,7,135,27,78,112,81,153,55,2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\ No newline at end of file
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","transcript_type":"EVM","split":null,"pretty_public_inputs":{"rescaled_inputs":[],"inputs":[],"processed_inputs":[],"processed_params":[],"processed_outputs":[],"rescaled_outputs":[["0","0","0","0"]],"outputs":[["0x0000000000000000000000000000000000000000000000000000000000000000","0x0000000000000000000000000000000000000000000000000000000000000000","0x0000000000000000000000000000000000000000000000000000000000000000","0x0000000000000000000000000000000000000000000000000000000000000000"]]},"timestamp":1730948796270,"commitment":"KZG"}
\ No newline at end of file
diff --git a/tests/assets/settings.json b/tests/assets/settings.json
index a123fc99d..1fc77672e 100644
--- a/tests/assets/settings.json
+++ b/tests/assets/settings.json
@@ -27,7 +27,8 @@
         "check_mode": "UNSAFE",
         "commitment": "KZG",
         "decomp_base": 128,
-        "decomp_legs": 2
+        "decomp_legs": 2,
+        "bounded_log_lookup": false
     },
     "num_rows": 46,
     "total_assignments": 92,
diff --git a/tests/integration_tests.rs b/tests/integration_tests.rs
index 820d686ad..f7bfb8801 100644
--- a/tests/integration_tests.rs
+++ b/tests/integration_tests.rs
@@ -205,7 +205,7 @@ mod native_tests {
         "1l_tiny_div",
     ];
 
-    const TESTS: [&str; 95] = [
+    const TESTS: [&str; 96] = [
         "1l_mlp", //0
         "1l_slice",
         "1l_concat",
@@ -305,6 +305,7 @@ mod native_tests {
         "lstm_medium", // 92
         "lenet_5",     // 93
         "rsqrt",       // 94
+        "log",         // 95
     ];
 
     const WASM_TESTS: [&str; 46] = [
@@ -543,7 +544,7 @@ mod native_tests {
             }
         });
 
-            seq!(N in 0..=94 {
+            seq!(N in 0..=95 {
 
             #(#[test_case(TESTS[N])])*
             #[ignore]